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Superbizarre Is Not Superb: Improving BERT's Interpretations of Complex Words with Derivational Morphology
How does the input segmentation of pretrained language models (PLMs) aff...
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A Closer Look at Few-Shot Crosslingual Transfer: Variance, Benchmarks and Baselines
We present a focused study of few-shot crosslingual transfer, a recently...
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Few-Shot Text Generation with Pattern-Exploiting Training
Providing pretrained language models with simple task descriptions or pr...
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Subword Sampling for Low Resource Word Alignment
Annotation projection is an important area in NLP that can greatly contr...
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Automatically Identifying Words That Can Serve as Labels for Few-Shot Text Classification
A recent approach for few-shot text classification is to convert textual...
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Dynamic Contextualized Word Embeddings
Static word embeddings that represent words by a single vector cannot ca...
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Transformers Are Better Than Humans at Identifying Generated Text
Fake information spread via the internet and social media influences pub...
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It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners
When scaled to hundreds of billions of parameters, pretrained language m...
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Investigating Pretrained Language Models for Graph-to-Text Generation
Graph-to-text generation, a subtask of data-to-text generation, aims to ...
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Automatic Domain Adaptation Outperforms Manual Domain Adaptation for Predicting Financial Outcomes
In this paper, we automatically create sentiment dictionaries for predic...
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Neural Topic Modeling with Continual Lifelong Learning
Lifelong learning has recently attracted attention in building machine l...
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Explainable and Discourse Topic-aware Neural Language Understanding
Marrying topic models and language models exposes language understanding...
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Pre-trained Language Models as Symbolic Reasoners over Knowledge?
How can pre-trained language models (PLMs) learn factual knowledge from ...
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Modeling Graph Structure via Relative Position for Better Text Generation from Knowledge Graphs
We present a novel encoder-decoder architecture for graph-to-text genera...
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Unsupervised Embedding-based Detection of Lexical Semantic Changes
This paper describes EmbLexChange, a system introduced by the "Life-Lang...
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BERT-kNN: Adding a kNN Search Component to Pretrained Language Models for Better QA
Khandelwal et al. (2020) show that a k-nearest-neighbor (kNN) component ...
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Generating Derivational Morphology with BERT
Can BERT generate derivationally complex words? We present the first stu...
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Identifying Necessary Elements for BERT's Multilinguality
It has been shown that multilingual BERT (mBERT) yields high quality mul...
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Masking as an Efficient Alternative to Finetuning for Pretrained Language Models
We present an efficient method of utilizing pretrained language models, ...
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Quantifying the Contextualization of Word Representations with Semantic Class Probing
Pretrained language models have achieved a new state of the art on many ...
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SimAlign: High Quality Word Alignments without Parallel Training Data using Static and Contextualized Embeddings
Word alignments are useful for tasks like statistical and neural machine...
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Inexpensive Domain Adaptation of Pretrained Language Models: A Case Study on Biomedical Named Entity Recognition
Domain adaptation of Pretrained Language Models (PTLMs) is typically ach...
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Exploiting Cloze Questions for Few-Shot Text Classification and Natural Language Inference
Some NLP tasks can be solved in a fully unsupervised fashion by providin...
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Multipurpose Intelligent Process Automation via Conversational Assistant
Intelligent Process Automation (IPA) is an emerging technology with a pr...
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Extending Machine Language Models toward Human-Level Language Understanding
Language is central to human intelligence. We review recent breakthrough...
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Morphological Segmentation Inside-Out
Morphological segmentation has traditionally been modeled with non-hiera...
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Sentence Meta-Embeddings for Unsupervised Semantic Textual Similarity
We address the task of unsupervised Semantic Textual Similarity (STS) by...
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BERT is Not a Knowledge Base (Yet): Factual Knowledge vs. Name-Based Reasoning in Unsupervised QA
The BERT language model (LM) (Devlin et al., 2019) is surprisingly good ...
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Negated LAMA: Birds cannot fly
Pretrained language models have achieved remarkable improvements in a br...
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BERTRAM: Improved Word Embeddings Have Big Impact on Contextualized Model Performance
Pretraining deep contextualized representations using an unsupervised la...
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Linguistically Informed Relation Extraction and Neural Architectures for Nested Named Entity Recognition in BioNLP-OST 2019
Named Entity Recognition (NER) and Relation Extraction (RE) are essentia...
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Type-aware Convolutional Neural Networks for Slot Filling
The slot filling task aims at extracting answers for queries about entit...
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BioNLP-OST 2019 RDoC Tasks: Multi-grain Neural Relevance Ranking Using Topics and Attention Based Query-Document-Sentence Interactions
This paper presents our system details and results of participation in t...
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Multi-view and Multi-source Transfers in Neural Topic Modeling with Pretrained Topic and Word Embeddings
Though word embeddings and topics are complementary representations, sev...
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Multi-view and Multi-source Transfers in Neural Topic Modeling
Though word embeddings and topics are complementary representations, sev...
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Neural Architectures for Fine-Grained Propaganda Detection in News
This paper describes our system (MIC-CIS) details and results of partici...
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Morphological Word Embeddings
Linguistic similarity is multi-faceted. For instance, two words may be s...
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Probing for Semantic Classes: Diagnosing the Meaning Content of Word Embeddings
Word embeddings typically represent different meanings of a word in a si...
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Generating Multi-Sentence Abstractive Summaries of Interleaved Texts
In multi-participant postings, as in online chat conversations, several ...
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SherLIiC: A Typed Event-Focused Lexical Inference Benchmark for Evaluating Natural Language Inference
We present SherLIiC, a testbed for lexical inference in context (LIiC), ...
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Robust Argument Unit Recognition and Classification
Argument mining is generally performed on the sentence-level -- it is as...
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Unsupervised Text Generation from Structured Data
This work presents a joint solution to two challenging tasks: text gener...
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Analytical Methods for Interpretable Ultradense Word Embeddings
Word embeddings are useful for a wide variety of tasks, but they lack in...
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Rare Words: A Major Problem for Contextualized Embeddings And How to Fix it by Attentive Mimicking
Pretraining deep neural network architectures with a language modeling o...
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Attentive Mimicking: Better Word Embeddings by Attending to Informative Contexts
Learning high-quality embeddings for rare words is a hard problem becaus...
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Learning Semantic Representations for Novel Words: Leveraging Both Form and Context
Word embeddings are a key component of high-performing natural language ...
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CIS at TAC Cold Start 2015: Neural Networks and Coreference Resolution for Slot Filling
This paper describes the CIS slot filling system for the TAC Cold Start ...
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A Stronger Baseline for Multilingual Word Embeddings
Levy, Søgaard and Goldberg's (2017) S-ID (sentence ID) method applies wo...
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Aligning Very Small Parallel Corpora Using Cross-Lingual Word Embeddings and a Monogamy Objective
Count-based word alignment methods, such as the IBM models or fast-align...
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Multi-Multi-View Learning: Multilingual and Multi-Representation Entity Typing
Knowledge bases (KBs) are paramount in NLP. We employ multiview learning...
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