Pre-trained language representations such as ELMo, OpenAI GPT, BERT , ERNIE 1.0 111In order to distinguish our new ERNIE 2.0 framework and the ERNIE model, the latter is referred to as ERNIE 1.0. and XLNet have been proven to be effective for improving the performances of various natural language understanding tasks including sentiment classification , natural language inference 8] and so on.
Generally, the pre-training of models often train the model based on the co-occurrence of words and sentences. While in fact, there are other lexical, syntactic and semantic information worth examining in training corpora other than co-occurrence. For example, named entities like person names, location names, and organization names, may contain conceptual information. Information like sentence order and proximity between sentences enable the models to learn structure-aware representations. Whereas, semantic similarity at the document level or discourse relations among sentences allow the models to learn semantic-aware representations. In order to discover all valuable information in training corpora, be it lexical, syntactic or semantic representations, we propose a continual pre-training framework named ERNIE 2.0 which could incrementally build and train a large variety of pre-training tasks through constant multi-task learning.
Our ERNIE framework supports the introduction of various customized tasks at any time. These tasks share the same encoding networks and are trained through multi-task learning. This method makes the encoding of lexical, syntactic and semantic information across different tasks possible. Moreover, when given a new task, our framework could incrementally train the distributed representations according to the previous training parameters that it grasped.
In summary, our contributions are as follows:
We propose a continual pre-training framework ERNIE 2.0, which supports customized training tasks and multi-task pre-training in an incremental way.
We construct several unsupervised language processing tasks to verify the effectiveness of the proposed framework. Experimental results demonstrate that ERNIE 2.0 achieves significant improvements over BERT and XLNet on 16 tasks including English GLUE benchmarks and several Chinese tasks.
Our fine-tuning code of ERNIE 2.0 and models pre-trained on English corpora are available at https://github.com/PaddlePaddle/ERNIE.
2 Related Work
2.1 Unsupervised Transfer Learning for Language Representation
It is effective to learn general language representation by pre-training a language model with a large amount of unannotated data. Traditional methods usually focus on context-independent word embedding. Methods such as Word2Vec and GloVe learn fixed word embeddings through word co-occurrence on large corpora.
Recently, several studies centered on contextualized language representations have been proposed and context-dependent language representations have shown state-of-the-art results in various natural language processing tasks. ELMo proposes to extract context-sensitive features from a language model. OpenAI GPT enhances the context-sensitive embedding by adjusting the Transformer. BERT, however, adopts a masked language model while adding a next sentence prediction task into the pre-training. XLM integrates two methods to learn cross-lingual language models, namely the unsupervised method that relies only on monolingual data and supervised method that leverages parallel bilingual data. MT-DNN achieves a better result through learning several supervised tasks in GLUE together based on the pre-trained model, which eventually leads to improvements on other supervised tasks that are not learned in the stage of multi-task supervised fine-tuning. XLNet uses Transformer-XL and proposes a generalized autoregressive pre-training method that learns bidirectional contexts by maximizing the expected likelihood over all permutations of the factorization order.
2.2 Continual Learning
Continual learning[16, 17] aims to train the model with several tasks in sequence so that it remembers the previously-learned tasks when learning the new ones. This method is inspired by the learning process of humans, as humans are capable of continuously accumulating the information acquired by study or experience to efficiently develop new skills. With continual learning, the model should be able to performs well on new tasks thanks to the knowledge acquired during previous training.
3 The ERNIE 2.0 Framework
As shown in Figure 1, the ERNIE 2.0 framework is built based on an architecture of pre-training and fine-tuning which recently grows in popularity in natural language processing. ERNIE 2.0 differs from traditional pre-training methods in that, instead of training with a small number of pre-training objectives, it could constantly introduce a large variety of pre-training tasks to help the model efficiently learn the lexical, syntactic and semantic representations. On top of that, ERNIE 2.0 framework keeps updating the pre-trained model with multi-task learning. During fine-tuning, the ERNIE model is first initialized with the pre-trained parameters, and would be later fine-tuned using data from specific tasks.
3.1 Continual Pre-training
The process of continual pre-training contains two steps. Firstly, We continually construct unsupervised pre-training tasks with big data and prior knowledge involved. Secondly, We incrementally update the ERNIE model via multi-task learning.
For pre-training task construction, we construct different kinds of tasks including word-aware tasks, structure-aware tasks and semantic-aware tasks222For the detailed information of these tasks, please refer to the next section.. All of these pre-training tasks rely on self-supervised or weak-supervised signals that could be obtained from massive data without human annotation. For multi-task pre-training, the ERNIE 2.0 framework trains all of these tasks in a continuous learning paradigm. Specifically, we would first train an initial model with a simple task before constantly introducing new pre-training tasks to upgrade the model. When adding a new task, we initialize the parameters of the previous one. Whenever a new task is introduced, it would be trained with the previous ones to make sure that the model does not forget the knowledge it has learnt. In this way, the ERNIE framework would be able to keep learning and accumulating the knowledge acquired during the process, and the accumulation of knowledge would enable the models to perform better during new tasks.
As shown in Figure 2
, the architecture of continual pre-training contains a series of shared text encoding layers to encode contextual information, which can be customized by using recurrent neural networks or a deep Transformer consisting of stacked self-attention layers. The parameters of the encoder can be updated across all pre-training tasks.
There are two kinds of loss functions in our framework. One is the sequence-level loss and the other one is the token-level loss, which are similar to the loss functions of BERT. Each pre-training task has its own loss function. During pre-training, one or more sentence-level loss functions can be combined with multiple token-level loss functions to continually update the model.
3.2 Fine-tuning for Application Tasks
By virtue of fine-tuning with task-specific supervised data, the pre-trained model can be adapted to different language understanding tasks, such as question answering, natural language inference, and semantic similarity. Each downstream task has its own fine-tuned models after being fine-tuned.
4 ERNIE 2.0 Model
In order to verify the effectiveness of the framework, we construct several unsupervised language processing tasks and develop a pre-trained model called ERNIE 2.0 model. In this section we introduce the implementation of the model in the proposed framework.
4.1 Model Structure
Transformer Encoder The model uses a multi-layer Transformer as the basic encoder like other pre-training models such as GPT, BERT and XLM. The transformer can capture the contextual information for each token in the sequence via self-attention, and generate a sequence of contextual embeddings. Given a sequence, the special classification embedding [CLS] is added to the first place of the sequence. Furthermore, the symbol of [SEP] is added as the separator in the intervals of the segments for the multiple input segment tasks.
Task Embedding The model feeds task embedding to modulate the characteristic of different tasks. We represent different tasks with an id ranging from 0 to N. Each task id is assigned to one unique task embedding. The corresponding token, segment, position and task embedding are taken as the input of the model. We can use any task id to initialize our model in the fine-tuning process. The model structure is shown in Figure 3.
4.2 Pre-training Tasks
We construct several tasks to capture different aspects of information in the training corpora. The word-aware tasks teach the model to capture the lexical information, the structure-aware tasks teach the model to capture the syntactic information of the corpus and the semantic-aware tasks are in charge of semantic signals. These pre-training tasks will be described in detail in the following part.
4.2.1 Word-aware Pre-training Tasks
Knowledge Masking Task ERNIE 1.0 propose an effective strategy to enhance representation through knowledge integration. It introduced phrase masking and named entity masking and predicts the whole masked phrases and named entities to help the model learn the dependency information in both local contexts and global contexts. We use this task to train an initial version of the model.
Capitalization Prediction Task Capitalized words usually have certain specific semantic value compared to other words in sentences. The cased model has some advantages in tasks like named entity recognition while the uncased model is more suitable for some other tasks. To combine the advantages of both models, we add a task to predict whether the word is capitalized or not.
Token-Document Relation Prediction Task We add a task to predict whether the token in a segment appears in other segments of the original document. Empirically, the words that appear in many parts of a document are usually commonly-used words or relevant with the main topics of the document. Therefore, through identifying the key words of a document appearing in the segment, the task can enable the ability of a model to capture the key words of the document to some extent.
4.2.2 Structure-aware Pre-training Tasks
Sentence Reordering Task We add a sentence reordering task to learn the relationships among sentences. During the pre-training process of this task, a given paragraph is randomly split into 1 to m segments and then all of the combinations are shuffled by a random permuted order. We let the pre-trained model to reorganize these permuted segments, modeled as a k-class classification problem where . Empirically, the sentences reordering task can enable the pre-trained model to learn relationships among sentences in a document.
Sentence Distance Task We also construct a pre-training task to learn the sentence distance using document-level information. This task is modeled as a 3-class classification problem. "0" represents that the two sentences are adjacent in the same document, "1" represent that the two sentences are in the same document, but not adjacent, and "2" represents that the two sentences are from two different documents.
4.2.3 Semantic-aware Pre-training Tasks
Discourse Relation Task Beside the distance task mentioned above, we introduce a task to predict the semantic or rhetorical relation between two sentences. We use the data built by Sileo et.al to train a pre-trained model for English tasks. Following the method in Sileo et.al, we also automatically construct a Chinese dataset for pre-training.
IR Relevance Task We build a pre-training task to learn the short text relevance in information retrieval. It is a 3-class classification task which predicts the relationship between a query and a title. We take the query as the first sentence and the title as the second sentence. The search log data from Baidu Search Engine is used as our pre-training data. There are three kinds of labels in this task. The query and title pairs that are labelled as " 0" stand for strong relevance, which means that the title is clicked by the users after they input the query. Those labelled as "1" represent weak relevance, which implies that when the query is input by the users, these titles appear in the search results but failed to be clicked by users. The label "2" means that the query and title are completely irrelevant and random in terms of semantic information.
We compare the performance of ERNIE 2.0 with the state-of-the-art pre-training models. For English tasks, we compare our results with BERT and XLNet on GLUE. For Chinese tasks, we compare the results with that of BERT and the previous ERNIE 1.0 model on several Chinese datasets.
5.1 Pre-training and Implementation
5.1.1 Pre-training Data
Similar to that of BERT, some data in the English corpus are crawled from Wikipedia and BookCorpus. On top of that we collect some from Reddit. We also use the Discovery data as our discourse relation data. For the Chinese corpus, we collect a variety of data, such as encyclopedia, news, dialogue, information retrieval and discourse relation data from Baidu Search Engine. The details of the pre-training data are shown in Table 1.
5.1.2 Pre-training Settings
To compare with BERT, We use the same model settings of transformer as BERT. The base model contains 12 layers, 12 self-attention heads and 768-dimensional of hidden size while the large model contains 24 layers, 16 self-attention heads and 1024-dimensional of hidden size. The model settings of XLNet are same as BERT.
ERNIE 2.0 is trained on 48 NVidia v100 GPU cards for the base model and 64 NVidia v100 GPU cards for the large model in both English and Chinese. The ERNIE 2.0 framework is implemented on PaddlePaddle, which is an end-to-end open source deep learning platform developed by Baidu. We use Adam optimizer that parameters of which are fixed to, , with a batch size of 393216 tokens. The learning rate is set as 5e-5 for English model and 1.28e-4 for Chinese model. It is scheduled by decay scheme noam with warmup over the first 4,000 steps for every pre-training task. By virtue of float16 operations, we manage to accelerate the training and reduce the memory usage of our models. Each of the pre-training tasks is trained until the metrics of pre-training tasks converges.
|IR Relevance Data||-||4500M|
|Discourse Relation Data||171M||1110M|
5.2 Fine-tuning Tasks
5.2.1 English Task
As a multi-task benchmark and analysis platform for natural language understanding, General Language Understanding Evaluation (GLUE) is usually applied to evaluate the performance of models. We also test the performance of ERNIE 2.0 on GLUE. Specifically, GLUE covers a diverse range of NLP datasets, including:
CoLA: The Corpus of Linguistic Acceptability (CoLA) consists of 10657 sentences from 23 linguistics, annotated for acceptability (grammatically) by their original authors. CoLA is commonly used in the task of judging whether a sentence conforms to the syntax specification.
MNLI: Multi-genre Natural Language Inference (MNLI) is a crowd-sourced collection of 433k sentence pairs annotated with textual entailment information, and is usually used for textual inference tasks.
RTE: Recognizing Textual Entailment (RTE) is a corpus similar to MNLI and is usually used for Natural Language Inference task.
WNLI: Winograd Natural Language Inference (WNLI) is a corpus that captures the coreference information between two paragraphs.
QQP: Quora Question Pairs (QQP)333https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs consists of over 400,000 sentence pairs with data extracted from Quora QA community and is commonly used in tasks for judging whether the question pairs are duplicates or not.
MRPC: Microsoft Research Paraphrase Corpus (MRPC) contains 5800 pairs of sentences extracted from news on the Internet and is annotated to capture the equivalence of paraphrase or semantic relationship between a pair of sentences. MRPC is commonly used in similar tasks as QQP.
STS-B: The Semantic Textual Similarity Benchmark (STS-B) contains a selection of the English datasets. These datasets contain texts from image captions, news headlines and user forums.
AX: It is a auxilary hand-crafted diagnostic test suite that enables detailed linguistic analysis of models.
Table 2 is the detailed information of the datasets in GLUE.
5.2.2 Chinese Tasks
We executed extensive experiments on 9 Chinese NLP tasks, including machine reading comprehension, named entity recognition, natural language inference, semantic similarity, sentiment analysis and question answering. Specifically, the following Chinese datasets are chosen to evaluate the performance of ERNIE 2.0 on Chinese tasks:
Named Entity Recognition (NER): MSRA-NER (SIGHAN 2006) 
Natural Language Inference (NLI): XNLI 
Sentiment Analysis (SA): ChnSentiCorp 444https://github.com/pengming617/bert_classification
Question Answering (QA): NLPCC-DBQA 555http://tcci.ccf.org.cn/conference/2016/dldoc/evagline2.pdf
The details of these datasets are shown in Table 3.
Machine Reading Comprehension Machine Reading Comprehension (MRC) is a representative document-level modeling task which aims to extract continuous segments from the given text to answer the questions. For MRC task, CMRC 2018 , DRCD , DuReader  are used as test datasets.
CMRC 2018: Chinese Machine Reading Comprehension 2018 (CMRC 2018) is an extractive reading comprehension dataset for machine reading comprehension, and it is released by the Chinese Information Processing Society of China, IFLYTEK and Harbin Institute of Technology.
DRCD: Delta Reading Comprehension Dataset (DRCD) is also an extractive reading comprehension dataset which is released by Delta Research Institute. Notably, DRCD is a Traditional Chinese dataset, so we pre-converted it to simplified Chinese using a released tool666 https://github.com/skydark/nstools/tree/master/zhtools.
DuReader: DuReader is a large-scale real-world Chinese dataset for machine reading comprehension and question answering, which is released by Baidu at ACL 2018. All questions in the dataset are sampled from real anonymous user queries and the answers to the questions are manually generated. The experiments are carried out on the subset of DuReader for MRC.
Named Entity Recognition Named Entity Recognition (NER) aims to recognize various entities, including person names, location names and organization names and so on. It can be seen as a sequence labeling task. For the NER task, the MSRA-NER (SIGHAN 2006) dataset released by Microsoft Research Asia is chosen.
Natural Language Inference Natural Language Inference (NLI) aims to determine the semantic relationship (entailment, contradiction and neutral) between two sentences or two words. For the NLI task, we choose the popular XNLI  dataset, which is a crowd-sourced collection for the MultiNLI corpus and the pairs in the dataset are annotated with textual entailment and translated into 14 languages including Chinese.
Sentiment Analysis Sentiment Analysis (SA) aims to analyze whether the sentiment of a sentence is positive or negative. SA can be simply regraded as a binary classification task. For the SA task, we use the ChnSentiCorp dataset, which includes comments in several domains such as hotels, books, and electronic computers.
Semantic Similarity Semantic Similarity (SS) aims to identify whether two sentences have the same intention based on the likeness of their meaning or semantic content. For the SS task, we use the LCQMC  and BQ Corpus  datasets. LCQMC is released by Harbin Institute of Technology at COLTING 2018. BQ Corpus is jointly released by Harbin Institute of Technology and WeBank at EMNLP 2018. Each pair of sentences in the two datasets is associated with a binary label indicating whether the two sentences share the same intention.
Question Answering Question Answering (QA) aims to select answers for the corresponding questions. For the QA task, we use the NLPCC-DBQA dataset, which is released at NLPCC in 2016.
5.3 Implementation Details
|Epoch||Learning Rate||Batch Size||Epoch||Learning Rate||Batch Size|
|Epoch||Learning Rate||Batch Size||Epoch||Learning Rate||Batch Size|
5.4 Experimental Results
5.4.1 Results on English Tasks
In order to ensure the integrity of the experiments, we evaluate the performance of the base models and the large models of each comparison method on GLUE. Notably, considering the fact that only the results of the single model XLNet on the dev set are reported, we can only compare the performance of ERNIE 2.0 and XLNet on the dev set. In order to obtain a fair comparison with BERT and XLNet, we run a single-task and single-model777which mean the result without additional tricks such as ensemble and multi-task losses. ERNIE 2.0 on the dev set. The detailed results on GLUE are depicted in Table 6.
As shown in the BASE model columns of Table 6, ERNIE 2.0 outperforms BERT on all of the 10 tasks and obtains a score of 80.6. As shown in the dev columns of LARGE model section in Table 6, ERNIE 2.0 consistently outperforms BERT and XLNet on all of 8 tasks except MNLI-m. Furthermore, as shown in the test columns of LARGE model section in Table 6, ERNIE 2.0 outperforms BERT on all of the 10 tasks, with an improvement of 10.1 points, 8.4 points, 3.0 points, 2.9 points, 2.7 points, 2.0 points and 1.9 points on RTE, AX, CoLA, MNLI-mm, WNLI, MNLI-m and QNLI respectively. Concretely, ERNIE 2.0 gets a score of 83.6 on the GLUE test set and achieves a 3.1% improvement over the previous SOTA pre-training model BERT.
|Task(Metrics)||BASE model||LARGE model|
|BERT||ERNIE 2.0||BERT||XLNet||ERNIE 2.0||BERT||ERNIE 2.0|
|CoLA (Matthew Corr.)||52.1||55.2||60.6||63.6||65.4||60.5||63.5|
|STS-B (Pearson Corr./Spearman Corr.)||87.1/85.8||87.6/86.5||90.0/-||91.8/-||92.3/-||87.6/86.5||91.2/90.6|
5.4.2 Results on Chinese Tasks
Table 7 shows the performances on 9 classical Chinese NLP tasks. It can be seen that ERNIE 1.0 outperforms BERT on XNLI, MSRA-NER, ChnSentiCorp, LCQMC and NLPCC-DBQA tasks, yet the performance is less ideal on the rest, which is caused by the difference in pre-training between the two methods. Specifically, the pre-training data of ERNIE 1.0 does not contain instances whose length exceeds 128, but BERT is pre-trained with the instances whose length are 512. From the results, it can be also seen that the proposed ERNIE 2.0 makes further progress, which significantly outperforms BERT on all of the nine tasks. Furthermore, ERNIE 2.0 achieves the best performance and creates new state-of-the-art results on these Chinese NLP tasks. Specifically, ERNIE 2.0 yields improvements of more than 2 points over BERT on the CMRC 2018, DRCD, DuReader, XNLI, MSRA-NER and LCQMC tasks, and yields improvements of more than 2 points over ERNIE on the CMRC 2018, DRCD, DuReader and XNLI tasks.
|Task||Metrics||BERT||ERNIE 1.0||ERNIE 2.0||ERNIE 2.0|
We proposed a continual pre-training framework named ERNIE 2.0, in which pre-training tasks can be incrementally built and learned through multi-task learning in a continual way. Based on the framework, we constructed several pre-training tasks covering different aspects of language and trained a new model called ERNIE 2.0 model which is more competent in language representation. ERNIE 2.0 was tested on the GLUE benchmarks and various Chinese tasks. It obtained significant improvements over BERT and XLNet. In the future, we will introduce more pre-training tasks to the ERNIE 2.0 framework to further improve the performance of the model.
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