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Found 20 papers, 13 papers with code
The Thieves on Sesame Street are Polyglots - Extracting Multilingual Models from Monolingual APIs
Pre-training in natural language processing makes it easier for an adversary with only query access to a victim model to reconstruct a local copy of the victim by training with gibberish input data paired with the victim{'}s labels for that data.
Joint Energy-based Model Training for Better Calibrated Natural Language Understanding Models
In this work, we explore joint energy-based model (EBM) training during the finetuning of pretrained text encoders (e. g., Roberta) for natural language understanding (NLU) tasks.
CTRLsum: Towards Generic Controllable Text Summarization
Our approach enables users to control multiple aspects of generated summaries by interacting with the summarization system through textual input in the form of a set of keywords or descriptive prompts.
What's New? Summarizing Contributions in Scientific Literature
To overcome this problem, we introduce a new task of disentangled paper summarization, which seeks to generate separate summaries for the paper contributions and the context of the work, making it easier to identify the key findings shared in articles.
Char2Subword: Extending the Subword Embedding Space Using Robust Character Compositionality
To alleviate these challenges, we propose a character-based subword module (char2subword) that learns the subword embedding table in pre-trained models like BERT.
GeDi: Generative Discriminator Guided Sequence Generation
While large-scale language models (LMs) are able to imitate the distribution of natural language well enough to generate realistic text, it is difficult to control which regions of the distribution they generate.
SummEval: Re-evaluating Summarization Evaluation
The scarcity of comprehensive up-to-date studies on evaluation metrics for text summarization and the lack of consensus regarding evaluation protocols continue to inhibit progress.
Double-Hard Debias: Tailoring Word Embeddings for Gender Bias Mitigation
Word embeddings derived from human-generated corpora inherit strong gender bias which can be further amplified by downstream models.
A Simple Language Model for Task-Oriented Dialogue
Task-oriented dialogue is often decomposed into three tasks: understanding user input, deciding actions, and generating a response.
Ranked #2 on
Response Generation
on MMConv
ProGen: Language Modeling for Protein Generation
Generative modeling for protein engineering is key to solving fundamental problems in synthetic biology, medicine, and material science.
BERT is Not an Interlingua and the Bias of Tokenization
Multilingual transfer learning can benefit both high- and low-resource languages, but the source of these improvements is not well understood.
Evaluating the Factual Consistency of Abstractive Text Summarization
Currently used metrics for assessing summarization algorithms do not account for whether summaries are factually consistent with source documents.
CTRL: A Conditional Transformer Language Model for Controllable Generation
Large-scale language models show promising text generation capabilities, but users cannot easily control particular aspects of the generated text.
Neural Text Summarization: A Critical Evaluation
Text summarization aims at compressing long documents into a shorter form that conveys the most important parts of the original document.
Explain Yourself! Leveraging Language Models for Commonsense Reasoning
Deep learning models perform poorly on tasks that require commonsense reasoning, which often necessitates some form of world-knowledge or reasoning over information not immediately present in the input.
Ranked #22 on
Common Sense Reasoning
on CommonsenseQA
XLDA: Cross-Lingual Data Augmentation for Natural Language Inference and Question Answering
XLDA is in contrast to, and performs markedly better than, a more naive approach that aggregates examples in various languages in a way that each example is solely in one language.
Cross-Lingual Natural Language Inference
Data Augmentation
+3
Unifying Question Answering, Text Classification, and Regression via Span Extraction
Even as pre-trained language encoders such as BERT are shared across many tasks, the output layers of question answering, text classification, and regression models are significantly different.
The Natural Language Decathlon: Multitask Learning as Question Answering
Though designed for decaNLP, MQAN also achieves state of the art results on the WikiSQL semantic parsing task in the single-task setting.
Revisiting Activation Regularization for Language RNNs
Both of these techniques require minimal modification to existing RNN architectures and result in performance improvements comparable or superior to more complicated regularization techniques or custom cell architectures.
Learned in Translation: Contextualized Word Vectors
For fine-grained sentiment analysis and entailment, CoVe improves performance of our baseline models to the state of the art.
Ranked #9 on
Text Classification
on TREC-6
