Articles

In this paper, we propose a novel self- supervised opinion summarization framework TransSum, which models opinion sum- maries as translations operating on the low-dimensional aspect and sentiment em- bedding spaces. Specifically, we propose two contrastive objectives to learn the cru- cial aspect and sentiment embeddings of reviews, by taking advantage …...

Chinese Spell Checking (CSC) aims to de- tect and correct erroneous characters for user- generated text in Chinese language. Most of the Chinese spelling errors are misused se- mantically, phonetically or graphically similar characters. Previous attempts notice this phe- nomenon and try to utilize the similarity rela- tionship for this …...

BERT has been used for solving common- sense tasks such as CommonsenseQA. While prior research has found that BERT does con- tain commonsense information to some extent, there has been work showing that pre-trained models can rely on spurious associations (e.g., data bias) rather than key cues in solving sen- …...

Emotion cause analysis (ECA) has been an emerging topic in natural language processing, which aims to identify the reasons behind a certain emotion expressed in the text. Most ECA methods intend to identify the clause which contains the cause of a given emotion, but such clause-level ECA (CECA) can be …...

In multi-hop QA, answering complex ques- tions entails iterative document retrieval for finding the missing entity of the question. The main steps of this process are sub-question detection, document retrieval for the sub- question, and generation of a new query for the final document retrieval. However, building a dataset that …...

Modern task-oriented semantic parsing ap- proaches typically use seq2seq transformers to map textual utterances to semantic frames comprised of intents and slots. While these models are empirically strong, their specific strengths and weaknesses have largely re- mained unexplored. In this work, we study BART (Lewis et al., 2020) and XLM-R …...

Pre-trained Transformer-based neural lan- guage models, such as BERT, have achieved remarkable results on varieties of NLP tasks. Recent works have shown that attention-based models can benefit from more focused atten- tion over local regions. Most of them restrict the attention scope within a linear span, or confine to certain …...

Machine Reading Comprehension (MRC) is an important testbed for evaluating models’ natural language understanding (NLU) ability. There has been rapid progress in this area, with new models achieving impressive perfor- mance on various benchmarks. However, ex- isting benchmarks only evaluate models on in- domain test sets without considering their ro- …...

Existing rumor detection strategies typically provide detection labels while ignoring their explanation. Nonetheless, providing pieces of evidence to explain why a suspicious tweet is rumor is essential. As such, a novel model, LOSIRD, was proposed in this paper. First, LOSIRD mines appropriate evidence sentences and classifies them by automatically checking …...

Answering questions about why characters perform certain actions is central to under- standing and reasoning about narratives. De- spite recent progress in QA, it is not clear if existing models have the ability to answer “why” questions that may require common- sense knowledge external to the input narra- tive. In …...

Respiratory insufficiency is a symptom that requires hospitalization. This work investi- gates whether it is possible to detect this condi- tion by analyzing patient’s speech samples; the analysis was performed on data collected dur- ing the first wave of the COVID-19 pandemic in 2020, and thus limited to respiratory insuf- …...

Much of NLP research has focused on crowd- sourced static datasets and the supervised learning paradigm of training once and then evaluating test performance. As argued in de Vries et al. (2020), crowdsourced data has the issues of lack of naturalness and relevance to real-world use cases, while the static …...

Cross-language entity linking grounds men- tions written in several languages to a mono- lingual knowledge base. We use a simple neu- ral ranking architecture for this task that uses multilingual BERT representations of both the mention and the context as input, so as to explore the ability of a transformer …...

In the Chinese medical insurance industry, the assessor’s role is essential and requires sig- nificant efforts to converse with the claimant. This is a highly professional job that involves many parts, such as identifying personal infor- mation, collecting related evidence, and mak- ing a final insurance report. Due to the …...

The performance of state-of-the-art neural rankers can deteriorate substantially when ex- posed to noisy inputs or applied to a new domain. In this paper, we present a novel method for fine-tuning neural rankers that can significantly improve their robustness to out-of-domain data and query perturbations. Specifically, a contrastive loss that …...

Paraphrase generation is an and challenging NLG problem.important In this work, we propose a new Identification-then- Aggregation (IA) framework to tackle this task. In the identification step, the input tokens are sorted into two groups by a novel Primary/Secondary Identification (PSI) algorithm. In the aggregation step, these groups are separately …...

Document-level relation extraction has at- tracted much attention in recent years. It is usu- ally formulated as a classification problem that predicts relations for all entity pairs in the doc- ument. However, previous works indiscrimi- nately represent intra- and inter-sentential re- lations in the same way, confounding the dif- ferent …...

We present a novel method for relation extrac- tion (RE) from a single sentence, mapping the sentence and two given entities to a canon- ical fact in a knowledge graph (KG). Espe- cially in this presumed sentential RE setting, the context of a single sentence is often sparse. This paper …...

Automatic math problem solving has re- cently attracted increasing attention as a long- standing AI benchmark. In this paper, we fo- cus on solving geometric problems, which re- quires a comprehensive understanding of tex- tual descriptions, visual diagrams, and theo- rem knowledge. However, the existing meth- ods were highly dependent …...

Structural syntax knowledge has been proven effective for semantic role labeling (SRL), while existing works mostly use only one sin- gleton syntax, such as either syntactic depen- dency or constituency tree. In this paper, we explore the integration of heterogeneous syn- tactic representations for SRL. We first con- sider a …...