Articles

Non-autoregressive translation (NAT) signif- icantly accelerates the inference process via predicting the entire target sequence. How- ever, recent studies show that NAT is weak at learning high-mode of knowledge such as one-to-many translations. We argue that modes can be divided into various granu- larities which can be learned from easy …...

Dialogue generation has been improved through injecting knowledge into generative models. However, addition of knowledge through simple selection of sentences or para- graphs is likely to introduce noise and dimin- ish the effectiveness of the generative models. In this paper, we present a novel Knowledge Term Weighting Model (KTWM) that …...

Among the various modes of communication in social media, the use of Internet memes has emerged as a powerful means to convey politi- cal, psychological, and socio-cultural opinions. Although memes are typically humorous in na- ture, recent days have witnessed a proliferation of harmful memes targeted to abuse various social …...

Increasing studies leverage pre-trained lan- guage models and meta-learning frameworks to solve few-shot text classification problems. Most of the current studies focus on building a meta-learner from the information of input texts but ignore abundant semantic informa- tion beneath class labels. In this work, we show that class-label information can …...

Discovering whether words are semantically related and identifying the specific semantic re- lation that holds between them is of crucial im- portance for automatic reasoning on text data. For that purpose, different methodologies have been proposed that either (1) tackle feature en- gineering, (2) fine-tune latent semantic spaces, or (3) …...

As languages evolve historically, making com- putational approaches sensitive to time can improve performance on specific tasks. In this work, we assess whether applying histor- ical language models and time-aware meth- ods help with determining the correct sense of polysemous words. We outline the task of time-sensitive Targeted Sense Disambigua- …...

Multilingual text summarization requires the ability to understand documents in multiple languages and generate summaries in the corresponding language, which poses more challenges on current summarization systems. However, this problem has been rarely stud- ied due to the lack of large-scale supervised summarization data in multiple languages. In this paper, …...

Neural machine translation suffers when paral- lel data for training is scarce. Previous works have explored transfer learning to assist train- ing in low-resource scenarios. However, they transfer either from high-resource parallel data, or from monolingual data. In this work, we pro- pose a framework to transfer multiple sources of …...

Ever since the advent of deep learning, cross- modal representation learning has been dom- inated by the approaches involving convolu- tional neural networks for visual representa- tion and recurrent neural networks for lan- guage representation. Transformer architec- ture, however, has rapidly taken over the re- current neural networks in natural …...

Recently, chest X-ray report generation, which aims to automatically generate descriptions of given chest X-ray images, has received grow- ing research interests. The key challenge of chest X-ray report generation is to accurately capture and describe the abnormal regions. In most cases, the normal regions dominate the entire chest X-ray …...

Aspect Term Extraction (ATE), Opinion Term Extraction (OTE) and Aspect Sentiment Clas- sification (ASC) are the essential building blocks of Aspect-based Sentiment Analy- sis (ABSA). They are typically treated as sep- arate tasks and are individually studied by previous work. Recent studies intend to in- corporate multiple sub-tasks into a …...

This work empirically explores effective ex- ploiting of intermediate output from pre- trained language models (PrLMs) for language generation tasks. For this purpose, we propose an improved method to integrate public check- points of PrLMs for the most convenience and perform extensive experiments on 6 differ- ent kinds of PrLMs, …...

As part of scientific articles, grant informa- tion refers to funder names and their cor- responding grant numbers. Extracting such funding information from articles is of signif- icant importance to both academic and fund- ing bodies. The studies on this topic face two major challenges: 1) no high-quality bench- mark …...

Pre-trained language models (PrLM) has been shown powerful in enhancing a broad range of downstream tasks including various dia- logue related ones. However, PrLMs are usu- ally trained on general plain text with com- mon language model (LM) training objectives, which cannot sufficiently capture dialogue ex- clusive features due to …...

In this paper, we propose a novel edge-editing approach to extract relation information from a document. We treat the relations in a doc- ument as a relation graph among entities in this approach. The relation graph is itera- tively constructed by editing edges of an initial graph, which might be …...

Syntactic and semantic structure directly re- flect relations expressed by the text at hand and are thus very useful for the relation extrac- tion (RE) task. Their symbolic nature allows increased interpretability for end-users and de- velopers, which is particularly appealing in RE. Although they have been somewhat overshad- owed …...

Learning low-dimensional representations of networked documents is a crucial task for doc- uments linked in network structures. Rela- tional Topic Models (RTMs) have shown their strengths in modeling both document contents and relations to discover the latent topic se- mantic representations. However, higher-order correlation structure information among doc- uments is …...

The attention layer has become a prevalent component in improving the effectiveness of neural network models for NLP tasks. Figur- ing out why attention is effective and its inter- pretability has attracted a widespread deliber- ation. Current studies mostly investigate the effect of attention mechanism based on the at- tention …...

Although distant supervision automatically generates training data for relation extraction, it also introduces false-positive (FP) and false- negative (FN) training instances to the gener- ated datasets. While both types of errors de- grade the final model performance, previous work on distant supervision denoising focuses more on suppressing FP noise and …...

Current researches on frame semantic parsing include three subtasks, namely frame identifi- cation, argument identification and role clas- sification. Most of previous systems process these subtasks independently and ignore their interactions. We introduce a novel architec- ture based on multi-decoder strategy to han- dle these subtasks together. The multi-decoder strategy …...