Augmented Natural Language for Generative Sequence Labeling

by   Ben Athiwaratkun, et al.

We propose a generative framework for joint sequence labeling and sentence-level classification. Our model performs multiple sequence labeling tasks at once using a single, shared natural language output space. Unlike prior discriminative methods, our model naturally incorporates label semantics and shares knowledge across tasks. Our framework is general purpose, performing well on few-shot, low-resource, and high-resource tasks. We demonstrate these advantages on popular named entity recognition, slot labeling, and intent classification benchmarks. We set a new state-of-the-art for few-shot slot labeling, improving substantially upon the previous 5-shot (75.0%→ 90.9%) and 1-shot (70.4%→ 81.0%) state-of-the-art results. Furthermore, our model generates large improvements (46.27%→ 63.83%) in low-resource slot labeling over a BERT baseline by incorporating label semantics. We also maintain competitive results on high-resource tasks, performing within two points of the state-of-the-art on all tasks and setting a new state-of-the-art on the SNIPS dataset.




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1 Introduction

Transfer learning has been the pinnacle of recent successes in natural language processing. Large pre-trained language models are powerful backbones that can be fine-tuned for different tasks to achieve state-of-the-art performance in wide-raging applications (Peters et al., 2018; Devlin et al., 2019; Radford et al., 2019; Lewis et al., 2019; Yang et al., 2019; Liu et al., 2019).

While these models can be adapted to perform many tasks, each task is often associated to its own output space, which limits the ability to perform multiple tasks at the same time. For instance, a sentiment analysis model is normally a binary classifier that decides between class labels

“positive” and “negative”, while a multi-class entailment system classifies each input as “entail”, “contradict”, or “neither”. This approach presents difficulty in knowledge sharing among tasks. That is, to train the model for a new task, the top-layer classifier is replaced with a new one that corresponds to novel classes. The class types are specified implicitly through different indices in the new classifier, which contain no prior information about the label meanings. This discriminative approach does not incorporate label name semantics and often requires a non-trivial amount of examples to train (Lee et al., 2020). While this transfer learning approach has been immensely successful, a more efficient approach should incorporate prior knowledge when possible.

Conditional generative modeling is a natural way to incorporate prior information and encode the output of multiple tasks in a shared predictive space. Recent work by Raffel et al. (2019) built a model called t5 to perform multiple tasks at once using natural language as its output. The model differentiates tasks by using prefixes in its input such as “classify sentiment:”, “summarize:”, or “translate from English to German:” and classify each input by generating natural words such as “positive” for sentiment classification or “This article describes …” for summarization.

However, the appropriate output format for important sequence labeling applications in NLP, such as named entity recognition (NER) and slot labeling (SL) is not immediately clear. In this work, we propose an augmented natural language format for sequence labeling tasks. Our format locally tags words within the sentence (Figure 1) and is easily extensible to sentence-level classification tasks, such as intent classification (IC).

Figure 1: The conversion between the canonical BIO tagging format and our augmented natural language format.

Our highlighted contributions and main findings are as follows:

  1. [1)]

  2. We propose an effective new output format to perform joint sequence labeling and sentence classification through a generation framework.

  3. We demonstrate the ability to perform multiple tasks such as named entity recognition, slot labeling and intent classification within a single model.

  4. Our approach is highly effective in low-resource settings. Even without incorporating label type semantics as priors, the generative framework learns more efficiently than a token-level classification baseline. The model improves further given natural word labels, indicating the benefits of rich semantic information.

  5. We show that supervised training on related sequence labeling tasks acts as an effective meta-learner that prepares the model to generate the appropriate output format. Learning each new task becomes much easier and results in significant performance gains.

  6. We set a new state-of-the-art for few-shot slot labeling, outperforming the prior state-of-the-art by a large margin.

  7. We plan to open source our implementation and will update the paper with a link to our repository.

Figure 2: Comparison between our generative-style sequence labeling model (top) and the conventional token-level classification model (bottom).

2 Model

Sequence Labeling as Generation

Most work on sequence labeling uses token-level classification frameworks. That is, given a list of tokens , we perform a prediction on every token to obtain where is a token-level prediction function. The prediction is accurate if it matches the original sequence label .

In contrast to this convention, we frame sequence labeling as a conditional sequence generation problem where given the token list , we generate an output list where is a sequence-to-sequence model. A “naive” formulation for this task would be to directly generate given . However, this approach is prone to errors such as word misalignment and length mismatch (see supplementary materials Section A.2 for discussion).

We propose a new formulation for this generation task such that, given the input sequence , our method generates output in augmented natural language. The augmented output repeats the original input sequence with additional markers that indicate the token-spans and their associated labels. More specifically, we use the format [   ] to indicate that the token sequence is labeled as .

Fig. 1 depicts the proposed format and its equivalent canonical BIO format for the same input sentence. The conversion between the BIO format and our augmented natural language format is invertible without any information loss. This is crucial so that the generated output from model prediction can be converted back for comparison without uncertainty.

There are other formats which can encapsulate all the tagging information but are not invertible. For instance, outputting only the token spans of interest with tagging patterns [   ] without repeating the entire sentence results in the invertibility breaking down when there are duplicate token spans with different labels. We discuss this further in the appendix Section A.3.

Joint Sequence Classification and Labeling

Our sequence to sequence approach also supports joint sentence classification and sequence labeling by incorporating the sentence-level label in the augmented natural language format. In practice, we use the pattern (( sentence-level label )) in the beginning of the generated sentence, as shown in Fig. 1. The use of double parentheses is to prevent confusion with a single parenthesis that can occur in the original word sequence .

Training and Evaluation

We train our model by adapting the pre-trained t5 with the sequence to sequence framework. Additionally, we prefix the input with task descriptors in order to simultaneously perform multiple classification and labeling tasks, similar to the approach by Raffel et al. (2019). This results in a seamless multi-task framework, as illustrated in the top part of Fig. 2. To evaluate, we convert the generated output back to the canonical BIO format and calculate the F1 score for sequence labeling or accuracy for sentence classification.

Natural Labels

Labels are associated to real-world concepts that can be described through natural words. These words have rich information, but are often ignored in traditional discriminative approaches. In contrast, our model naturally incorporate label semantics directly through the generation-as-classification approach.

We perform label mapping in order to match the labels to its natural descriptions and use the natural labels in the augmented natural language output. Our motivation is as follows: (1) Pre-trained conditional generation models which we adapt on have richer semantics embedded in natural words, rather than dataset-specific label names. For instance, “country city state” contains more semantic information compared to “GPE”, which is an original label in named entity recognition tasks. Using natural labels should allow the model to learn the association between word tokens and labels more efficiently, without requiring many examples. (2) Label knowledge can be shared among different tasks. For instance, after learning how to label names as “person”, given a new task in another domain which requires labeling “artist”, the model can more easily associate names with “artist” due to the proximity of “person” and “artist” in embeddings. This is not the case if the concept of “person” was learned with other uninformative words.

Task & Dataset Intent Clas. Slot Labeling


Bi-Model (Wang et al., 2018) 98.99 96.89
Joint BERT (Chen et al., 2019) 98.60 97.50 97.00 96.10
ELMO+BiLSTM (Siddhant et al., 2019) 99.29 97.42 93.90 95.62


Cloze-CNN (Baevski et al., 2019) 93.50
BERT-MRC (Li et al., 2019a) 93.04 91.11
BERT-MRC + DSC (Li et al., 2019b) 93.33 92.07
BERT Base (Devlin et al., 2019) 92.40 88.95
Ours: Individual 99.00 96.86 97.43 96.13 90.70 90.24
Ours: SNIPS+ATIS 99.29 97.20 97.21 95.83
Ours: CoNLL+Ontonotes 91.48 89.52
Ours: SNIPS+ATIS+CoNLL+Ontonotes 99.14 97.08 96.82 96.65 91.48 89.67
Table 1: Results of our models trained on combinations of datasets. Results for Ours: individual are from models trained on a single respective dataset. We underline scores of our models that exceed previous state-of-the-art results in each domain. Scores in boldface are the best overall scores among our models, or among the baselines. We use the boldface and underline notation for the rest of the paper.

3 Related Work

Sequence to sequence learning has various applications including machine translation (Sutskever et al., 2014; Bahdanau et al., 2015), language modeling (Radford et al., 2018; Raffel et al., 2019), abstractive summarization (Rush et al., 2015), generative question answering (Dong et al., 2019), to name a few. However, the sequence-to-sequence framework is often not a method of choice when it comes to sequence labeling. Most models for sequence labeling use the token-level classification framework, where the model predicts a label for each element in the input sequence (Baevski et al., 2019; Li et al., 2019b; Chen et al., 2019). While select prior work adopts the sequence-to-sequence method for sequence labeling (Chen and Moschitti, 2018), this approach is not widely in use due to the difficulty of fixing the output length, output space, and alignment with the original sequence.

Multi-task and multi-domain learning often benefit sequence labeling performance (Changpinyo et al., 2018). The archetypal multi-task setup jointly trains on a target dataset and one or more auxiliary datasets. In the cross lingual setting, these auxiliary datasets typically represent high-resource languages (Schuster et al., 2018; Cotterell and Duh, 2017). While in a monolingual scenario, the auxiliary datasets commonly represent similar, high-resource tasks. Examples of similar multi-task pairs include NER and slot labeling (Louvan and Magnini, 2019) as well as dialogue state tracking and language understanding (Rastogi et al., 2018).

A recent series of works frame natural language processing tasks, such as translation, question answering, and sentence classification, as conditional sequence generation problems (Raffel et al., 2019; Radford et al., 2019; Brown et al., 2020). By unifying the model output space across tasks to consist of natural language symbols, these approaches reduce the gap between language model pre-training tasks and downstream tasks. Moreover, this framework allows acquisition of new tasks without any architectural change. The GPT-3 model (Brown et al., 2020) demonstrates the promise of this framework for few-shot learning. Among other successes, GPT-3 outperforms BERT-Large on the SuperGLUE benchmark using only 32 examples per task. To the best of our knowledge, we are the first to apply this multi-task conditional sequence generation framework to sequence labeling.

The conditional sequence generation framework makes it easy to incorporate label semantics, in the form of label names such as departure city, example values like San Francisco, and descriptions like “the city from which the user would like to depart on the airline”. Label semantics provide contextual signals that can improve model performance in multi-task and low-resource scenarios. Multiple works show that conditioning input representations on slot description embeddings improves multi-domain slot labeling performance (Bapna et al., 2017; Lee and Jha, 2019). Embedding example slot values in addition to slot descriptions yields further improvements in zero-shot slot labeling (Shah et al., 2019). In contrast to our work, these approaches train slot description and slot value embedding matrices, whereas our framework can incorporate these signals as natural language input without changing the network architecture.

4 Experimental Setup and Results

4.1 Data


We use popular benchmark data SNIPS (Coucke et al., 2018) and ATIS (Hemphill et al., 1990) for slot labeling and intent classification. SNIPS is an SLU benchmark with 7 intents and 39 distinct types of slots, while ATIS is a benchmark for the air travel domain (see appendix A.4 for details). We also evaluate our approach on two named entity recognition datasets, Ontonotes (Pradhan et al., 2013) and CoNLL-2003 (Sang and Meulder, 2003).

Construction of Natural Labels

We preprocess the original labels to natural words as follows. For Ontonotes and CoNLL datasets, we transform the original labels via mappings detailed in Table 9 and 5 in the appendix. For instance, we map “PER” to “person” and “GPE” to “country city state”. For SNIPS and ATIS, we use the following rules to convert intent and slot labels: (1) we split words based on “.”, “_”, “/”, and capitalized letters. For instance, we convert “object_type” to “object type” and “AddToPlaylist” to “add to playlist”. These rules result in better tokenization and enrich the label semantics. We refer to these as the natural label setting and use is as our default.

4.2 Multi-Task Sequence Classification and Slot Labeling

We first demonstrate that our model can perform multiple tasks in our generative framework and achieve highly competitive or state-of-the-art performance. We consider 4 sequence labeling tasks and 2 classification tasks: NER on Ontonotes and CoNLL datasets; and slot labeling (SL) and intent classification (IC) on SNIPS and ATIS dialog datasets. For comparison, we provide baseline results from the following models:

SL and IC: Bi-Model (Wang et al., 2018) uses two correlated bidirectional LSTMs to perform both IC and SL. Joint BERT (Chen et al., 2019) performs joint IC and SL with a sequential classifier on top of BERT, where the classification for the start-of-sentence token corresponds to intent class. ELMO+Bi-LSTM (Siddhant et al., 2019) uses a Bi-LSTM with CRF as a classifier on top of pre-trained ELMO (Peters et al., 2018).

NER: Cloze-CNN (Baevski et al., 2019) fine-tunes a Bi-LSTM with CRF model Peters et al. (2018) on a pre-trained model with a cloze-style word reconstruction task. BERT MRC (Li et al., 2019a) performs sequence labeling in a question answering model to predict the slot label span. BERT MRC + Dice Loss (Li et al., 2019b) improves upon BERT MRC with a dice loss shown to be suitable for data with imbalanced labels. BERT (Devlin et al., 2019) refers to a token-level classification with BERT pre-trained model. Note that the results for BERT with Ontonotes are from our own implementation.

Figure 3:

Model performance on limited amount of training data. The error bars indicate the standard deviation over

random trials. Ours-o is our model with its original labels. Ours-n is our model with numeric labels.

In Table 1, we report a summary of the results for our method and the baselines. Our proposed model achieves highly competitive results for ATIS, Ontonotes, and CoNLL datasets, as well as state-of-the-art slot labeling and intent classification performance on the SNIPS dataset. Unlike all the baseline models, which can perform a single task on a specific dataset, our model can perform all the tasks considered at once (last row of Table 1). For the multi-task models, our results show that different sequence labeling task can help mutually benefit each other, where ATIS slot labeling result improves from 96.13 to 96.65 and CoNLL improves from 90.70 to 91.48. While there are other approaches that perform better than our models in some tasks, we highlight the simplicity of our generation framework which performs multiple tasks seamlessly. This ability helps the models transfer knowledge among tasks with limited data, which are demonstrated through the rest of the paper.

4.3 Limited Resource Scenarios and Importance of Label Semantics

In this section, we show that our model can use the semantics of labels to learn efficiently, which is crucial for scenarios with limited labeled data. To demonstrate this effect, we use our model with the following variants of labels which differ semantic quality: (1) natural label, (2) original label and (3) numeric label.

The natural label version is our default setting where we use labels expressed in natural words. The original label case uses labels provided by the datasets, and the numeric label case uses numbers as label types. In the numeric version, the model does not have pre-trained semantics of the label types and has to learn the associations between the labels and the relevant words from scratch. We also compare with the BERT token-level classification model. Similar to the numeric label case, the label types for BERT do not initially have associated semantics and are implicit through indices in the classifier weights. We use the SNIPS dataset to conduct our experiments due to its balanced domains (see Table 7 in Appendix). We experiment with very limited resource scenarios where we use as low as of training data, corresponding to roughly one training sentence per label type on average.

Figure 2(a) shows the sequence labeling performance for varying amount of training data (see Table 10 in the appendix for numeric results). We observe that label semantics play a crucial role in the model’s ability to learn effectively for limited resource scenarios. Our model with natural labels outperforms all other models, achieving an F1 score of with training data, and giving a slight boost over using original labels (). We believe that the improvement can be more dramatic in other datasets where the original labels have no meanings (such as in the numeric case), are heavily abbreviated, or contain rare words. With the numeric model, the performance suffers significantly in low-resource settings, achieving only , or lower than the natural label model, with data. This result further supports the importance of label semantics in our generation approach. Interestingly, we also observe that the numeric model still outperforms BERT token-level classification (), where neither model contains prior label semantics. This result indicates that even in the absence of label meanings, the generation approach seems more suitable than the token-level framework.

4.4 Teaching Model to Generate via Supervised Transfer Learning

While we train our model in limited data scenarios, we are asking the model to generate a new format of output given small amount of data. This is challenging since a sequence generation framework typically requires large amount of training (Sutskever et al., 2014). Despite this challenge, our model is able to outperform the classical token-level framework with ease. This section explores a clear untapped potential – by teaching our model how to generate the augmented natural language format before adapting to new tasks, we show that the performance on limited data significantly improves. This result contrasts with the BERT token-level model where supervised transfer learning hurts overall performance compared to BERT’s initial pre-training due to possible overfitting.

To conduct this experiment, we train our model with the Ontonotes NER task in order to teach it the expected output format. Then, we adapt it on another task (SNIPS) with limited data, as in Section 4.3. We compare the results with the token-level BERT model, which also uses the BERT model trained on Ontonotes for supervised pre-training. We demonstrate the results in Figure 2(b) as well as highlight the improvement due to supervised pre-training in Figure 2(c). We also provide full numeric results in the appendix Table 11 for reference.

Our model demonstrates consistent improvement, achieving an F1 score of using of the training dataset, compared to without supervised transfer learning. The improvement trend also continues for other data settings, as shown in Figure 2(c). The benefits from transfer learning is particularly strong for the numeric label model, achieving compared to for data. This results suggests that the initial knowledge from supervised pre-training helps the model associate its labels (without prior semantics) to the associated words more easily.

The supervised transfer learning can also be seen as a meta-learner, which teaches the model how to perform sequence labeling in the generative style. In fact, when we investigate the model output without adapting to the SNIPS dataset, in addition to the output having the correct format, it already contains relevant tagging information for new tasks.

For instance, a phrase “Onto jerry’s Classical Moments in Movies” from the SNIPS dataset results in the model output “Onto jerry’s

[ Classical Moments in Movies work of art ]”. This prediction closely matches the true label “Onto [ jerry’s playlist owner ] [ Classical Moments in Movies playlist ]” where the true class of “Classical Moments in Movies” is playlist. Intuitively, the classification as work of art is in agreement with the true label playlist, but simply needs to be refined to match the allowed labels for the new task.

In contrast to our framework where the supervised transfer learning helps teach the model an output style, the transfer learning for the token-level classification simply adapts its weights and retains the same token-level structure (albeit with a new classifier). We observe no significant improvement from supervised pre-training for the BERT token-level model, which obtains an F1 score of compared to without supervised pre-training (with SNIPS data). The improvements are also close to zero or negative for higher data settings (Figure 2(c)), suggesting that the pre-training of the token-level classification might overfit to the supervised data, and results in lower generalization on other downstream tasks. Overall, the final result on the BERT model lags far behind our framework, performing lower than our model’s score for training data.

In addition, our model with numeric labels performs much better than the BERT token-level model and further highlights the suitability of our generative output format for sequence labeling, regardless to the label semantics. Possible explanations are that the sequence to sequence label is less prone to overfitting compared to the classification framework. It could also be the case that locally tagging words with labels in the word sequence helps improve attention within the transformers model, and improve robustness to limited data.

We Mu Pl Bo Se Re Cr Ave.


TransferBERT 55.82 38.01 45.65 31.63 21.96 41.79 38.53 39.06
MN + BERT 21.74 10.68 39.71 58.15 24.21 32.88 69.66 36.72
WPZ + BERT 46.72 40.07 50.78 68.73 60.81 55.58 67.67 55.77
L-TapNet+CDT 71.53 60.56 66.27 84.54 76.27 70.79 62.89 70.41
Ours + SNIPS 82.62 77.46 71.33 85.49 83.22 84.23 82.92 81.04
Ours + Onto 56.39 67.10 53.49 71.94 66.21 69.04 28.80 59.00
Ours + No Meta 46.42 59.02 47.47 63.79 49.42 64.45 17.60 49.74


TransferBERT 59.41 42.00 46.70 20.74 28.20 67.75 58.61 46.11
MN + BERT 36.67 33.67 52.60 60.09 38.42 33.28 72.10 47.98
WPZ + BERT 67.82 55.99 46.02 72.17 73.59 60.18 66.89 63.24
L-TapNet+CDT 71.64 67.16 75.88 84.38 82.58 70.05 73.41 75.01
Ours + SNIPS 91.35 86.73 87.20 95.85 92.71 91.23 91.55 90.95
Ours + Onto 83.15 86.15 80.36 90.27 84.87 85.89 68.08 82.68
Ours + No Meta 73.14 82.02 78.82 84.86 83.14 86.63 52.56 77.31
Table 2: Our few-shot slot labeling results on domains of SNIPS dataset. Ours + SNIPS perform meta-training on the leave-one-out SNIPS data, similar to other baselines. Ours + Onto is our model trained on Ontonotes. Ours + No Meta involves no meta-training.

4.5 Few-Shot Sequence Labeling

4.5.1 Few-Shot Learning

In few-shot learning, we seek to train models such that given a new task, the models are able to learn efficiently from few labels. Different tasks are sampled from various data domains which differ in terms of allowed labels and other nuances such as input styles.

We define a data domain as a set of labeled examples which has its set of allowed label types . Few-shot learning approaches are evaluated over many episodes of data, which represent a variety of novel tasks. Each episode consists of a support set containing -shot labeled samples, as well as a query set used for evaluation. Data from the evaluation episodes are drawn from the target domains , which the model has not previously seen.

To learn such models, we typically have access to another set of domains called the source domains , which can be used as the training resources. In order to train the model to learn multiple tasks well, many few-shot learning approaches use meta-learning, or a learning to learn approach, where the model is trained with many episodes drawn from the source domains in order to mimic the evaluation (Vinyals et al., 2016; Snell et al., 2017; Sung et al., 2018; Finn et al., 2017). We refer to this as the episodic training.

Another approach, called fine-tuning, trains the model on a regular training set from the source domains: . Given an episode at evaluation time, the model fine-tunes it on the support , typically with a new classifier constructed for the new task, and evaluates on .

4.5.2 Few-Shot Baselines

TransferBERT trains a token-level classification model by fine-tuning. Matching Net (MN) + BERT Vinyals et al. (2016) Given a word , the model classifies by finding the most similar word in the support set and predicts as the label of . The model also adapts the backbone model with episodic training. Warm Proto Zero (WPZ) + BERT Fritzler et al. (2019) uses token-level prototypical network (Snell et al., 2017), which classifies by comparing a word to each class centroid rather than individual sample embeddings. L-TapNet + CDT Hou et al. (2020) uses a CRF framework and leverages label semantics in representing labels to calculate emission scores and a collapsed dependency transfer method to calculate transition scores. We note that all baselines except for TransferBERT uses episodic meta-training whereas TransferBERT uses fine-tuning. All baseline results are taken from Hou et al. (2020).

Our model performs fine-tuning with the generation framework. The major difference between our model and a token-level classification model such as TransferBERT is that we do not require a new classifier for every novel task during the fine-tuning on the support set. The sequence generation approach allows us to use the entire model and adapt it to new tasks, where the initial embeddings contain high quality semantics and help the model transfer knowledge efficiently.

4.5.3 K-shot Episode Construction

Traditionally, the support set is often constructed in -shot formats where we use only instances of each label type. In sequence labeling problems, this definition is challenging due to the presence of multiple occurrences or multiple label types in a single sentence. We follow Hou et al. (2020) by using the following definition of a K-shot setting: All labels within the task appears at least times in and would appear less than times if any sentence is removed. We sample episodes from each domain according to this definition. Note that Hou et al. (2020)’s episodes are similar to ours, but preprocess the sentences by lowercasing and removing extra tokens such as commas (see details in Section A.6). Our model is flexible and can handle raw sentences; we therefore use the episodes from the original SNIPS dataset without any modifications.

4.5.4 Data

We perform few-shot experiments on the domains of the SNIPS dataset, namely, Weather (We), Music (Mu), Playlist (Pl), Book (Bo), ScreeningEvent (Se), Restaurant (Re), CreativeWork (Cr). To evaluate a model on domain , we meta-train the model on . We refer to this as the leave-one-out meta-training sets. All other baselines also use this meta-training data setup.

We note that the training set has data distributions that closely match since they are both drawn from the SNIPS dataset. We investigate more challenging scenarios where we use an alternative source as a meta-training set, as well as no meta-training. In particular, we choose Ontonotes NER task as the alternative source domain. The benefits of using this setup is such that it establishes a single meta-trained model that works across all evaluation domains, which we offer as a challenging benchmark for future research.

4.5.5 Few-Shot Results

Table 2 demonstrates the results for few-shot experiments. Our model outperforms previous state-of-the-art on every domain evaluated. In the 5-shot case, our model achieves an average F1 score of , exceeding the strongest baseline by . Even without meta-training, the model is able to perform on par with state-of-the-art models, achieving an F1 score of versus for the baseline. Training on an alternative source (NER task) also proves to be an effective meta-learning strategy, performing better than the best baseline by . These results indicate that our model is robust in its ability to learn sequence tagging on target domains that differ from sources. In the 1-shot case, our model achieves an average F1 score of , outperforming the best baseline significantly ( improvement).

We note that the average support sizes are around to sentences for the 5-shot case, and one to sentences for the 1-shot case (see Table 12 and 13 for details). The results are particularly impressive given that we adapt a large transformer model based on such limited number of samples. In comparison to other fine-tuning approaches such as TransferBERT, our model performs substantially better, indicating that our generative framework is a more data-efficient approach for sequence labeling.

5 Discussion and Future Work

Our experiments consistently show that the generation framework is suitable for sequence labeling and sets a new record for few-shot learning. Our model adapts to new tasks efficiently with limited samples, while incorporating the label semantics expressed in natural words. This is akin to how humans learn. For instance, we do not learn the concept of “person” from scratch in a new task, but have prior knowledge that “person” likely corresponds to names, and refine this concept through observations. The natural language output space allows us to retain the knowledge from previous tasks through shared embeddings, unlike the token-level model which needs new classifiers for novel tasks, resulting in a broken chain of knowledge.

Our approach naturally lends itself to life-long learning. The unified input-output format allows the model to incorporate new data from any domain. Moreover, it has the characteristics of a single, life-long learning model that works well on many levels of data, unlike other approaches that only perform well on few-shot or high-resource tasks. Our simple yet effective approach is also easily extensible to other applications such as multi-label classification, or structured prediction via nested tagging patterns.


Appendix A Supplementary Materials

a.1 Experiment Setup

We describe the experiment setup for reproducibility in this section. We use Huggingface’s t5-base conditional generation model as well as their trainer (transformers.Trainer

) with its default hyperparameters to train all our models. The trainer uses AdamW

(Kingma and Ba, 2015; Loshchilov and Hutter, 2019) and linear learning rate decay.

  • We use 8 V100 GPUs for all our experiments.

  • Maximum batch size per GPU = 8.

  • Maximum sequence length = 128 for all tasks except Ontonotes where we use 175.

  • The number of epochs in multi-task experiments is


  • The number of epochs for limited-resource experiments is scaled to have the same number of optimization steps as that of when we use the entire training set. For instance, if we use of the entire training set, we use epochs on that limited set.

  • We perform no preprocessing except for replacing the labels with the natural labels described in Section 4.1.

  • We use a library seqleval111 for F1 evaluation which supports many tagging format such as BIO, BIOES, etc.

  • We use bert-base-multilingual-cased for our BERT model.

Task Descriptor Prefixes

A task descriptor helps encode information about the allowed set of label types. For instance, CoNLL allows only 4 types of labels whereas the 18 label types in Ontonotes are more fine-grained. A task descriptor also help encode the nuances among labels; for example, the CoNLL dataset has only one tag type “LOC” for location whereas Ontonotes differentiate locations with “GPE” (countries, cities, states) and a general “LOC” (non-GPE locations, mountain ranges, bodies of water). By specifying a task descriptor, we allow the model to learn the implicit constraints in the data and help it be able to distinguish what task it should perform given an input sentence. We use the corresponding prefixes “SNIPS: ”, “ATIS: ”, “Ontonotes: ” or “CONLL: ”. We ensure that these prefixes can be tokenized given a pretrained tokenizer properly without the model having to use the unknown token. We demonstrate in 4.2 that the prefix tags allow us to perform slot labeling (and intent classification) for different datasets using a single model. For a model trained on only a single dataset, such prefix can be omitted and does not affect the performance.

a.2 A naive approach for sequence labeling as sequence to sequence

We consider training a sequence-to-sequence model where the output is the sequence of BIO tags. Table 3 demonstrates an example with model prediction. We find that the model often outputs predictions that are misaligned with the original sentence slots. This is due to the complex relationships with the tokenizer. For instance, the tokenized version of this sentence (using t5-base tokenizer) is of length whereas the original sentence length is . Learning to map the slot labels to the correct tokens can be challenging.

_It, _, a, but, s, _San, chi, h,
_Rural, _Township, _to, _the,
_northeast, _, ,, _the, _Ku,
ant, u, _area, _of, _Tai, pe,
i, _city
sentence label prediction
0 It O O
1 abuts O O
2 Sanchih B-GPE O
3 Rural I-GPE B-GPE
4 Township I-GPE I-GPE
5 to O I-GPE
6 the O O
7 northeast O O
8 , O O
9 the O O
10 Kuantu B-LOC O
11 area O B-GPE
12 of O O
13 Taipei B-GPE O
14 City I-GPE B-GPE
15 - - I-GPE
Table 3: Example of a sentence and its tagging label from Ontonotes. The prediction is generated from training a sequence-to-sequence model with a raw BIO format.
Statistics\Dataset SNIPS ATIS Ontonotes CONLL
No. Training Samples 13084 4478 59924 14041
No. Validation Samples 700 500 8528 3250
No. Test Samples 700 893 8262 3453
Average sentence length 9.05 11.28 18.11 14.53
# of slot types (w/o BIO prefixes) 39 83 18 4
# intent types 7 21 N/A N/A
Table 4: Dataset Statistics
CONLL-2003 slot types Natural-word label
LOC location
MISC miscellaneous
ORG organization
PER person
Table 5: Label mapping for CoNLL-2003.

a.3 Shortened Generative Format

We show a failure case for a shorted generative format discussed in 2 where we repeat only the tagged pattern . Consider the following input and label ,

These two men have two dollars
O O O O B-money O
Table 6: Top row: original sentence. Bottom row: slot labels.

If we repeat only the tagged pattern, then the output will be

[ two   money ] .

Given and , it is ambiguous whether the canonical label should associate with two for two dollars or two men.

Intent/Domain # sen. # sl. Slot types (without BIO tags)
GetWeather (We) 2100 9 timeRange, condition_description, country, geographic_poi, city, state, current_location, condition_temperature, spatial_relation
PlayMusic (Mu) 2100 9 genre, year, album, music_item, playlist, service, sort, artist, track
AddToPlaylist (Pl) 2042 5 entity_name, music_item, playlist, playlist_owner, artist
RateBook (Bo) 2056 7 object_type, object_part_of_series_type, object_select, rating_value, object_name, best_rating, rating_unit
SearchScreeningEvent (Se) 2059 7 timeRange, object_location_type, object_type, location_name, movie_name, spatial_relation, movie_type
BookRestaurant (Re) 2073 14 party_size_number, served_dish, timeRange, country, poi, cuisine, spatial_relation, city, restaurant_name, sort, restaurant_type, facility, party_size_description, state
SearchCreativeWork (Cr) 2054 2 object_type, object_name
Sum 14484 53
# Distinct slots 39
Table 7: The intent classes and the corresponding slot labels for the SNIPS dataset. # sen is the number of sentences in the entire dataset. # sl. is the number of slot types for each intent, excluding ’O’ (no tag). Note that sentences among different intent classes can share slot types and the number of slot types in total is 39 disregarding the BIO prefixes B- and I-.
Intent # sen. # sl. Slot types (without BIO tags)
atis_flight 4298 71 fromloc.city_name, toloc.city_name, round_trip, arrive_date.month_name, arrive_date.day_number, stoploc.city_name, arrive_time.time_relative, arrive_time.time, meal_description, depart_date.month_name, depart_date.day_number, airline_name, depart_time.period_of_day, depart_date.day_name, toloc.state_name, depart_time.time_relative, depart_time.time, depart_date.date_relative, or, class_type, fromloc.airport_name, flight_mod, meal, economy, city_name, airline_code, depart_date.today_relative, flight_stop, toloc.state_code, fromloc.state_name, toloc.airport_name, connect, arrive_date.day_name, fromloc.state_code, arrive_date.today_relative, depart_date.year, depart_time.start_time, depart_time.end_time, arrive_time.start_time, arrive_time.end_time, cost_relative, flight_days, mod, airport_name, aircraft_code, toloc.country_name, toloc.airport_code, return_date.date_relative, flight_number, fromloc.airport_code, arrive_time.period_of_day, depart_time.period_mod, flight_time, return_date.day_name, fare_amount, arrive_date.date_relative, arrive_time.period_mod, period_of_day, stoploc.state_code, fare_basis_code, stoploc.airport_name, return_time.period_mod, return_time.period_of_day, return_date.today_relative, return_date.month_name, return_date.day_number, compartment, day_name, airport_code, stoploc.airport_code, flight
atis_airfare 471 45 round_trip, fromloc.city_name, toloc.city_name, cost_relative, fare_amount, class_type, economy, airline_name, flight_mod, depart_time.time_relative, depart_time.time, arrive_date.month_name, arrive_date.day_number, airline_code, flight_number, stoploc.city_name, toloc.airport_name, depart_date.date_relative, depart_date.day_name, depart_date.month_name, depart_date.day_number, toloc.state_code, depart_time.period_of_day, flight_stop, fromloc.state_name, toloc.state_name, toloc.airport_code, aircraft_code, depart_date.year, arrive_time.time_relative, arrive_time.time, fromloc.airport_code, fromloc.airport_name, depart_date.today_relative, return_date.month_name, return_date.day_number, connect, meal, arrive_date.date_relative, arrive_date.day_name, or, depart_time.period_mod, flight_time, flight_days, fromloc.state_code
atis_ground_service 291 23 toloc.airport_name, city_name, fromloc.airport_name, toloc.city_name, state_code, transport_type, airport_name, fromloc.city_name, or, depart_date.date_relative, depart_date.day_name, time, depart_date.month_name, depart_date.day_number, today_relative, flight_time, state_name, period_of_day, time_relative, day_name, month_name, day_number, airport_code
atis_airline 195 36 fromloc.city_name, toloc.city_name, toloc.state_code, mod, depart_date.day_name, class_type, depart_date.today_relative, stoploc.city_name, aircraft_code, arrive_date.month_name, arrive_date.day_number, toloc.airport_name, fromloc.state_code, depart_time.period_of_day, airline_code, flight_number, depart_time.time_relative, depart_time.time, depart_date.month_name, depart_date.day_number, arrive_time.time, city_name, airport_name, flight_stop, arrive_time.period_of_day, fromloc.airport_code, airline_name, depart_date.date_relative, connect, flight_days, round_trip, cost_relative, fromloc.airport_name, depart_time.start_time, depart_time.end_time, toloc.state_name
atis_abbreviation 180 14 fare_basis_code, airport_code, airline_code, meal, meal_code, restriction_code, airline_name, aircraft_code, class_type, days_code, mod, fromloc.city_name, toloc.city_name, booking_class
atis_aircraft 90 23 fromloc.city_name, toloc.city_name, depart_time.time_relative, depart_time.time, toloc.state_code, airline_name, mod, class_type, depart_date.day_name, airline_code, flight_number, stoploc.city_name, depart_time.period_of_day, flight_mod, aircraft_code, arrive_time.time_relative, arrive_time.time, arrive_date.day_name, depart_date.month_name, depart_date.day_number, arrive_date.month_name, arrive_date.day_number, city_name
atis_flight_time 55 20 flight_time, airline_name, toloc.airport_code, depart_date.month_name, depart_date.day_number, fromloc.city_name, toloc.city_name, depart_date.day_name, depart_time.period_of_day, airline_code, flight_number, flight_mod, depart_date.date_relative, depart_time.time, fromloc.airport_name, aircraft_code, depart_time.time_relative, airport_name, class_type, meal_description
atis_quantity 54 25 airline_code, class_type, flight_stop, fromloc.city_name, toloc.city_name, arrive_date.month_name, arrive_date.day_number, depart_date.month_name, depart_date.day_number, economy, airline_name, round_trip, toloc.airport_name, depart_date.today_relative, arrive_time.time_relative, arrive_time.time, fare_basis_code, city_name, stoploc.city_name, flight_number, flight_days, depart_time.time_relative, depart_time.time, depart_time.period_of_day, aircraft_code
atis_airport 38 9 city_name, state_code, fromloc.city_name, mod, airport_name, toloc.city_name, flight_stop, state_name, airline_name
atis_capacity 37 5 fromloc.city_name, toloc.city_name, airline_name, aircraft_code, mod
atis_flight, atis_airfare 33 21 fromloc.city_name, toloc.city_name, airline_name, flight_number, depart_date.day_name, depart_date.month_name, depart_date.day_number, flight_mod, round_trip, depart_time.time_relative, depart_time.time, cost_relative, fare_amount, depart_date.date_relative, arrive_time.time_relative, arrive_time.time, depart_time.period_of_day, toloc.state_code, flight_stop, return_date.date_relative, return_date.day_name
atis_distance 30 8 fromloc.airport_name, toloc.city_name, fromloc.city_name, depart_time.time, depart_date.month_name, depart_date.day_number, city_name, airport_name
atis_city 25 11 city_name, airline_name, airport_code, fromloc.airport_code, fromloc.city_name, toloc.city_name, depart_time.time_relative, depart_time.time, depart_time.period_of_day, airport_name, class_type
atis_ground_fare 25 6 transport_type, city_name, fromloc.city_name, fromloc.airport_name, airport_name, toloc.city_name
atis_flight_no 20 22 toloc.city_name, fromloc.city_name, arrive_time.time_relative, arrive_time.time, fromloc.state_name, toloc.state_name, depart_date.day_name, depart_date.month_name, depart_date.day_number, airline_name, depart_time.time, flight_mod, toloc.state_code, flight_time, cost_relative, class_type, depart_time.time_relative, depart_time.period_of_day, stoploc.city_name, flight_number, or, depart_date.today_relative
atis_meal 12 12 meal, fromloc.city_name, toloc.airport_code, airline_name, flight_number, toloc.city_name, airline_code, arrive_time.time, toloc.state_code, depart_date.day_name, depart_time.period_of_day, meal_description
atis_restriction 6 6 restriction_code, cost_relative, round_trip, fromloc.city_name, toloc.city_name, fare_amount
atis_airline, atis_flight_no 2 7 fromloc.city_name, toloc.city_name, depart_date.date_relative, depart_date.month_name, depart_date.day_number, arrive_time.time_relative, arrive_time.time
atis_day_name 2 2 fromloc.city_name, toloc.city_name
atis_aircraft, atis_flight,atis_flight_no 1 5 fromloc.city_name, toloc.city_name, airline_name, depart_time.time_relative, depart_time.time
atis_cheapest 1 1 cost_relative
atis_ground_service, atis_ground_fare 1 1 fromloc.airport_name
atis_airfare, atis_flight_time 1 3 flight_time, fromloc.city_name, toloc.city_name
atis_airfare, atis_flight 1 4 airline_name, flight_number, fromloc.airport_code, toloc.airport_code
atis_flight, atis_airline 1 5 fromloc.city_name, toloc.city_name, depart_date.day_name, depart_time.time_relative, depart_time.time
atis_flight_no, atis_airline 1 5 fromloc.city_name, toloc.city_name, depart_date.day_name, depart_time.time_relative, depart_time.time
Sum 5871 390
# Distinct slots 83
Table 8: The intent classes and the corresponding slot labels for the ATIS dataset. # sen is the number of sentences in the entire dataset. # sl. is the number of slot types for each intent, excluding ‘O’ (no tag). Note that sentences among different intent classes can share slot types and the number of slot types in total is 83 disregarding the BIO prefixes B- and I-.

a.4 Dataset Details

We provide details on the statistics of datasets used in Table 4. We provide details on the intent and slot label types in Table 7 for the SNIPS dataset and Table 8 for the ATIS dataset. The tagging label types and their label mapping are listed in Table 9 for Ontonotes and 5 for CoNLL.

Ontonotes slot types Natural-word label Descriptions
CARDINAL cardinal Numerals that do not fall under another type
DATE date Absolute or relative dates or periods
EVENT event Named hurricanes, battles, wars, sports events, etc.
FAC facility Buildings, airports, highways, bridges, etc.
GPE country city state countries, cities, states
LANGUAGE language Any named language
LAW law Named documents made into laws
LOC location Non-GPE locations, mountain ranges, bodies of water
MONEY money Monetary values, including unit
NORP nationality religious political group Nationalities or religious or political groups
ORDINAL ordinal ”first”, ”second”
ORG organization Companies, agencies, institutions, etc.
PERCENT percent Percentage (including “%”)
PERSON person People, including fictional
PRODUCT product Vehicles, weapons, foods, etc. (Nor services)
QUANTITY quantity Measurements, as of weight or distance
TIME time Times smaller than a day
WORK_OF_ART work of art Titiles of books, songs, etc.
Table 9: Label mapping for Ontonotes. The descriptions are obtained from Weischedel et al. (2012)

a.5 Low-Resource Results

We provide full numeric results for low-resource experiments from Section 4.3 and 4.4 in Table 10 and Table 11 respectively.

Slot Labeling F1 Score
% no. sen s/t Ours Ours-o Ours-n BERT
0.25 33 0.84 60.37 2.66 57.49 2.37 50.12 5.29 44.73 6.43
0.5 65 1.68 73.69 1.51 72.02 1.22 67.03 2.41 60.12 2.77
1 131 3.35 83.53 1.63 83.32 0.68 81.52 0.60 73.94 1.34
2 262 6.71 89.06 0.79 88.69 1.07 87.97 1.21 84.30 0.97
5 654 16.77 93.35 0.21 92.98 0.45 92.47 0.30 90.99 0.69
10 1308 33.55 94.65 0.18 94.51 0.29 94.41 0.22 93.56 0.68
20 2617 67.10 95.55 0.20 95.14 0.24 95.55 0.36 94.77 0.15
40 5234 134.19 96.15 0.15 96.22 0.10 96.16 0.11 96.05 0.29
100 13084 335.49 96.71 0.05 96.96 0.10 97.01 0.10 96.73 0.16
Table 10: Test results of our models for under varying levels of training resources on the SNIPS training data. % and no. sen columns indicate the % of the original training data and number of training sentences. s/t indicates the number of sentences per slot label type.
Slot Labeling F1 Score
% no. sen s/t Ours Ours-o Ours-n BERT
0.25 33 0.8 63.83 2.62 62.67 2.06 57.39 2.90 46.27 3.56
0.5 65 1.7 76.60 0.79 75.89 1.32 72.46 2.70 60.25 1.89
1 131 3.4 84.90 0.71 85.08 0.20 84.69 0.62 73.55 1.50
2 262 6.7 89.37 0.65 89.24 0.32 89.36 0.65 82.63 1.42
5 654 16.8 93.74 0.23 93.21 0.17 93.63 0.32 89.83 0.43
10 1308 33.5 94.78 0.24 94.60 0.41 94.78 0.23 92.23 0.35
20 2617 67.1 95.70 0.32 95.66 0.18 95.87 0.18 94.39 0.34
40 5234 134.2 96.52 0.25 96.60 0.23 96.40 0.21 95.67 0.32
100 13084 335.5 97.27 0.05 97.16 0.18 97.29 0.20 96.44 0.23
Table 11: Test results of our models for under varying levels of training resources on the SNIPS training data, with supervised pre-training on Ontonotes NER dataset. % and no. sen columns indicate the % of the original training data and number of training sentences. s/t indicates the number of sentences per slot label type.

a.6 Few-Shot Experiments

We provide details on the data used for our few-shot experiments and the full results in this Section.

a.6.1 Episode Data

We use two data constructions: the original episodes from Hou et al. (2020) and our own constructed episodes with Hou et al. (2020)’s definition. We provide the episode statistics (Ave ) in Table 12 for both constructions which demonstrate that the support size are comparable for each domain. The major difference is that Hou et al. (2020) pre-processes data by lowercasing all letters and removing extra tokens such as commas and apostrophes. In addition, Hou et al. (2020) modify the BIO prefixes in cases where the tokenization splits a token with the “B-” prefix into two or more units. For instance, the token [“lora’s”] with tag [B-playlist_owner] becomes [“lora”, “s”] with tags [B-playlist_owner, I-playlist_owner]. This treatment considerably increases the number of tokens with “I-” tags in the episodes created by Hou et al. (2020). Both data have episodes with query sentences. We provide the results for our episodes with lowercased words and Hou’s episodes, which shows the similarity between two settings.

We note that the support size for some domains can be smaller than in other domains, according to Hou et al. (2020) K-shot definition. For instance, domain Cr has around sentences on average whereas domain Re has more than sentences. This is because for some domains, there can be many tags of the same types in a single sentence.

a.6.2 5-Shot Results

Table 12 details the full results on both episode data and multiple variations of our models. Ours + SNIPS is trained on leave-one-out dataset. For instance, when we evaluate on domain We, we train on other domains except for We. This is the setting used in all other baselines. Ours + Onto shows the results trained on Ontonotes NER task (see label types in Table 7), which is from a different domain that the SNIPS dataset. Ours w/o meta involves no additional meta-training and fine-tunes on our backbone model directly.

We Mu Pl Bo Se Re Cr Ave.

Hou et al. (2020)’s Episodes

Ave. 28.91 34.43 13.84 19.83 19.27 41.58 5.28
SimBERT 53.46 54.13 42.81 75.54 57.10 55.30 32.38 52.96
TransferBERT 59.41 42.00 46.70 20.74 28.20 67.75 58.61 46.11
Matching Net 36.67 33.67 52.60 60.09 38.42 33.28 72.10 47.98
WPZ + BERT 67.82 55.99 46.02 72.17 73.59 60.18 66.89 63.24
L-TapNet+CDT 71.64 67.16 75.88 84.38 82.58 70.05 73.41 75.01
Ours + SNIPS 87.66 81.62 83.37 89.72 86.80 86.14 73.02 84.05
7.35 9.75 8.75 5.23 8.33 6.30 11.79
Ours + Onto 77.87 79.71 79.87 84.18 75.84 78.71 40.23 73.77
7.98 8.54 9.08 5.64 8.35 7.54 14.59
Ours + No Meta 70.82 74.24 73.88 83.32 74.94 75.18 41.22 70.51
8.39 9.26 8.11 6.11 9.93 7.24 16.42

Our episodes

Ave. 24.92 32.49 12.56 18.44 17.18 37.06 5.70
Ours + SNIPS 91.35 86.73 87.20 95.85 92.71 91.23 91.55 90.95
6.13 6.52 6.59 3.13 5.45 5.86 7.25
Ours + Onto 83.15 86.15 80.36 90.27 84.87 85.89 68.08 82.68
6.52 6.15 10.87 5.12 7.98 6.52 24.50
Ours + No Meta 73.14 82.02 78.82 84.86 83.14 86.63 52.56 77.31
9.41 5.09 9.94 3.55 4.87 8.52 22.06
Ours-l + SNIPS 86.18 83.51 84.15 89.33 85.74 86.34 76.69 84.60
6.76 7.63 6.67 5.31 7.46 6.53 11.34
Ours-l + Onto 76.07 79.25 77.54 82.49 74.92 80.44 42.78 73.36
9.21 10.10 9.19 6.45 10.17 6.37 16.56
Ours-l + No Meta 68.56 76.17 74.63 81.02 74.47 78.88 38.60 70.33
11.06 9.20 8.21 7.41 9.16 7.67 17.39
Table 12: Our 5-shot slot tagging results on domains of the SNIPS dataset. We provide the average and the standard deviation of F1 scores over 100 episodes. Ours-l indicate that we use lowercased words for input sentences.

a.7 1-Shot Results

Our models tend to perform well when there are sufficient enough sentences to fine-tune on. For domain ‘Cr’ (SearchCreativeWork) where there are highly limited number of sentences ( sentences on average), our model does not perform well compared to other baselines. This observation is consistent with the 1-shot results, which we include in the appendix Section A.7 Table 13, where we typically have less than sentences in the support set. In this case, our model performs comparable to Warm Proto Zero with BERT on average but is outperformed by L-TapNet+CDT. Other techniques to improve on the 1-shot learning result include bootstrapping more sentences from unlabeled corpus with the labels from the support set for better optimization.

We Mu Pl Bo Se Re Cr Ave.

Hou et al. (2020)’s Episodes

Ave. 6.15 7.66 2.96 4.34 4.29 9.41 1.30
SimBERT 36.10 37.08 35.11 68.09 41.61 42.82 23.91 40.67
TransferBERT 55.82 38.01 45.65 31.63 21.96 41.79 38.53 39.06
Matching Net 21.74 10.68 39.71 58.15 24.21 32.88 69.66 36.72
WPZ + BERT 46.72 40.07 50.78 68.73 60.81 55.58 67.67 55.77
L-TapNet+CDT 71.53 60.56 66.27 84.54 76.27 70.79 62.89 70.41
Ours + SNIPS 77.81 74.66 67.81 79.60 71.05 78.61 64.07 73.37
7.21 7.46 7.22 6.13 7.88 5.58 10.53
Ours + Onto 50.56 68.54 56.11 70.17 55.66 64.78 12.86 54.10
9.53 6.75 10.52 5.96 5.76 6.65 11.45
Ours + No Meta 40.28 53.42 45.71 63.90 42.57 59.29 14.11 45.61
9.23 10.71 11.93 8.26 7.94 7.45 11.50

Our episodes

Ave. 6.17. 6.99 2.66 3.73 4.15 9.07 1.27
Ours + SNIPS 82.62 77.46 71.33 85.49 83.22 84.23 82.92 81.04
5.96 7.56 7.15 5.74 6.47 5.67 8.71
Ours + Onto 56.39 67.10 53.49 71.94 66.21 69.04 28.80 59.00
10.67 7.50 11.18 7.43 10.02 7.68 17.67
Ours + No Meta 46.42 59.02 47.47 63.79 49.42 64.45 17.60 49.74
9.52 9.45 11.01 7.33 10.32 7.63 7.63
Ours-l + SNIPS 77.42 73.45 67.05 76.85 72.54 79.54 63.44 72.90
6.09 8.18 8.26 5.93 8.97 5.62 10.19
Ours-l + Onto 50.65 62.58 50.55 65.42 50.23 61.82 18.59 51.41
8.47 7.94 8.32 6.71 9.83 6.92 12.15
Ours-l + No Meta 41.47 57.69 44.13 61.22 40.96 59.60 11.00 45.15
8.25 12.46 7.45 6.55 11.50 8.60 10.87
Table 13: Our 1-shot slot tagging results on Hou et al. (2020)’episodes and our own constructed episodes. We provide the average and the standard deviation of F1 scores over 100 episodes. Ours-l indicate that we use lowercased words for input sentences.