pwc-archive/papers-with-abstracts huggingface dataset:
CETA: A Consensus Enhanced Training Approach for Denoising in Distantly Supervised Relation Extraction
Authors: Ruri Liu, Shasha Mo, Jianwei Niu, Shengda Fan
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Classification, Denoising, Relation, Relation Extraction, Robust classification, Sentence

Distantly supervised relation extraction aims to extract relational facts from texts but suffers from noisy instances. Existing methods usually select reliable sentences that rely on potential noisy labels, resulting in wrongly selecting many noisy training instances or underutilizing a large amount of valuable training data. This paper proposes a sentence-level DSRE method beyond typical instance selection approaches by preventing samples from falling into the wrong classification space on the feature space. Specifically, a theorem for denoising and the corresponding implementation, named Consensus Enhanced Training Approach (CETA), are proposed in this paper. By training the model with CETA, samples of different classes are separated, and samples of the same class are closely clustered in the feature space. Thus the model can easily establish the robust classification boundary to prevent noisy labels from biasing wrongly labeled samples into the wrong classification space. This process is achieved by enhancing the classification consensus between two discrepant classifiers and does not depend on any potential noisy labels, thus avoiding the above two limitations. Extensive experiments on widely-used benchmarks have demonstrated that CETA significantly outperforms the previous methods and achieves new state-of-the-art results.
Event Causality Identification via Derivative Prompt Joint Learning
Authors: Shirong Shen, Heng Zhou, Tongtong Wu, Guilin Qi
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Event Causality Identification, Language Modeling, Language Modelling, Sentence

This paper studies event causality identification, which aims at predicting the causality relation for a pair of events in a sentence. Regarding event causality identification as a supervised classification task, most existing methods suffer from the problem of insufficient annotated data. In this paper, we propose a new derivative prompt joint learning model for event causality identification, which leverages potential causal knowledge in the pre-trained language model to tackle the data scarcity problem. Specifically, rather than external data or knowledge augmentation, we derive two relevant prompt tasks from event causality identification to enhance the model’s ability to identify explicit and implicit causality. We evaluate our model on two benchmark datasets and the results show that our model has great advantages over previous methods.
A Relation Extraction Dataset for Knowledge Extraction from Web Tables
Authors: Siffi Singh, Alham Fikri Aji, Gaurav Singh, Christos Christodoulopoulos
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Knowledge Graphs, Relation, Relation Extraction

Relational web-tables are significant sources of structural information that are widely used for relation extraction and population of facts into knowledge graphs. To transform the web-table data into knowledge, we need to identify the relations that exist between column pairs. Currently, there are only a handful of publicly available datasets with relations annotated against natural web-tables. Most datasets are constructed using synthetic tables that lack valuable metadata information, or are limited in size to be considered as a challenging evaluation set. In this paper, we present REDTab, the largest natural-table relation extraction dataset. We have annotated ~9K tables and ~22K column pairs using crowd sourced annotators from MTurk, which has 50x larger number of column pairs than the existing human-annotated benchmark. Our test set is specially designed to be challenging as observed in our experiment results using TaBERT. We publicly release REDTab as a benchmark for the evaluation process in relation extraction.
Automatic Keyphrase Generation by Incorporating Dual Copy Mechanisms in Sequence-to-Sequence Learning
Authors: Siyu Wang, Jianhui Jiang, Yao Huang, Yin Wang
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Keyphrase Generation

The keyphrase generation task is a challenging work that aims to generate a set of keyphrases for a piece of text. Many previous studies based on the sequence-to-sequence model were used to generate keyphrases, and they introduce a copy mechanism to achieve good results. However, we observed that most of the keyphrases are composed of some important words (seed words) in the source text, and if these words can be identified accurately and copied to create more keyphrases, the performance of the model might be improved. To address this challenge, we propose a DualCopyNet model, which introduces an additional sequence labeling layer for identifying seed words, and further copies the words for generating new keyphrases by dual copy mechanisms. Experimental results demonstrate that our model outperforms the baseline models and achieves an obvious performance improvement.
Dependency-aware Prototype Learning for Few-shot Relation Classification
Authors: Tianshu Yu, Min Yang, Xiaoyan Zhao
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Classification, Few-Shot Relation Classification, Relation, Relation Classification, Sentence

Few-shot relation classification aims to classify the relation type between two given entities in a sentence by training with a few labeled instances for each relation. However, most of existing models fail to distinguish multiple relations that co-exist in one sentence. This paper presents a novel dependency-aware prototype learning (DAPL) method for few-shot relation classification. Concretely, we utilize dependency trees and shortest dependency paths (SDP) as structural information to complement the contextualized representations of input sentences by using the dependency-aware embedding as attention inputs to learn attentive sentence representations. In addition, we introduce a gate controlled update mechanism to update the dependency-aware representations according to the output of each network layer. Extensive experiments on the FewRel dataset show that DAPL achieves substantially better performance than strong baselines. For reproducibility, we will release our code and data upon the publication of this paper at https://github.com/publicstaticvo/DAPL.
MECI: A Multilingual Dataset for Event Causality Identification
Authors: Viet Dac Lai, Amir Pouran Ben Veyseh, Minh Van Nguyen, Franck Dernoncourt, Thien Huu Nguyen
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Cross-Lingual Transfer, Event Causality Identification, Transfer Learning

Event Causality Identification (ECI) is the task of detecting causal relations between events mentioned in the text. Although this task has been extensively studied for English materials, it is under-explored for many other languages. A major reason for this issue is the lack of multilingual datasets that provide consistent annotations for event causality relations in multiple non-English languages. To address this issue, we introduce a new multilingual dataset for ECI, called MECI. The dataset employs consistent annotation guidelines for five typologically different languages, i.e., English, Danish, Spanish, Turkish, and Urdu. Our dataset thus enable a new research direction on cross-lingual transfer learning for ECI. Our extensive experiments demonstrate high quality for MECI that can provide ample research challenges and directions for future research. We will publicly release MECI to promote research on multilingual ECI.
Method Entity Extraction from Biomedical Texts
Authors: Waqar Bin Kalim, Robert E. Mercer
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Dependency Parsing, Sentence

In the field of Natural Language Processing (NLP), extracting method entities from biomedical text has been a challenging task. Scientific research papers commonly consist of complex keywords and domain-specific terminologies, and new terminologies are continuously appearing. In this research, we find method terminologies in biomedical text using both rule-based and machine learning techniques. We first use linguistic features to extract method sentence candidates from a large corpus of biomedical text. Then, we construct a silver standard biomedical corpus composed of these sentences. With a rule-based method that makes use of the Stanza dependency parsing module, we label the method entities in these sentences. Using this silver standard corpus we train two machine learning algorithms to automatically extract method entities from biomedical text. Our results show that it is possible to develop machine learning models that can automatically extract method entities to a reasonable accuracy without the need for a gold standard dataset.
Optimal Partial Transport Based Sentence Selection for Long-form Document Matching
Authors: Weijie Yu, Liang Pang, Jun Xu, Bing Su, Zhenhua Dong, Ji-Rong Wen
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Form, Sentence

One typical approach to long-form document matching is first conducting alignment between cross-document sentence pairs, and then aggregating all of the sentence-level matching signals. However, this approach could be problematic because the alignment between documents is partial — despite two documents as a whole are well-matched, most of the sentences could still be dissimilar. Those dissimilar sentences lead to spurious sentence-level matching signals which may overwhelm the real ones, increasing the difficulties of learning the matching function. Therefore, accurately selecting the key sentences for document matching is becoming a challenging issue. To address the issue, we propose a novel matching approach that equips existing document matching models with an Optimal Partial Transport (OPT) based component, namely OPT-Match, which selects the sentences that play a major role in matching. Enjoying the partial transport properties of OPT, the selected key sentences can not only effectively enhance the matching accuracy, but also be explained as the rationales for the matching results. Extensive experiments on four publicly available datasets demonstrated that existing methods equipped with OPT-Match consistently outperformed the corresponding underlying methods. Evaluations also showed that the key sentences selected by OPT-Match were consistent with human-provided rationales.
Cross-modal Contrastive Attention Model for Medical Report Generation
Authors: Xiao Song, Xiaodan Zhang, Junzhong Ji, Ying Liu, Pengxu Wei
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Image to text, Medical Report Generation

Medical report automatic generation has gained increasing interest recently as a way to help radiologists write reports more efficiently. However, this image-to-text task is rather challenging due to the typical data biases: 1) Normal physiological structures dominate the images, with only tiny abnormalities; 2) Normal descriptions accordingly dominate the reports. Existing methods have attempted to solve these problems, but they neglect to exploit useful information from similar historical cases. In this paper, we propose a novel Cross-modal Contrastive Attention (CMCA) model to capture both visual and semantic information from similar cases, with mainly two modules: a Visual Contrastive Attention Module for refining the unique abnormal regions compared to the retrieved case images; a Cross-modal Attention Module for matching the positive semantic information from the case reports. Extensive experiments on two widely-used benchmarks, IU X-Ray and MIMIC-CXR, demonstrate that the proposed model outperforms the state-of-the-art methods on almost all metrics. Further analyses also validate that our proposed model is able to improve the reports with more accurate abnormal findings and richer descriptions.
Document-Level Relation Extraction via Pair-Aware and Entity-Enhanced Representation Learning
Authors: Xiusheng Huang, Hang Yang, Yubo Chen, Jun Zhao, Kang Liu, Weijian Sun, Zuyu Zhao
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Document-level Relation Extraction, Relation, Relation Extraction, Representation Learning

Document-level relation extraction aims to recognize relations among multiple entity pairs from a whole piece of article. Recent methods achieve considerable performance but still suffer from two challenges: a) the relational entity pairs are sparse, b) the representation of entity pairs is insufficient. In this paper, we propose Pair-Aware and Entity-Enhanced(PAEE) model to solve the aforementioned two challenges. For the first challenge, we design a Pair-Aware Representation module to predict potential relational entity pairs, which constrains the relation extraction to the predicted entity pairs subset rather than all pairs; For the second, we introduce a Entity-Enhanced Representation module to assemble directional entity pairs and obtain a holistic understanding of the entire document. Experimental results show that our approach can obtain state-of-the-art performance on four benchmark datasets DocRED, DWIE, CDR and GDA.
Improving Zero-Shot Entity Linking Candidate Generation with Ultra-Fine Entity Type Information
Authors: Xuhui Sui, Ying Zhang, Kehui Song, Baohang Zhou, Guoqing Zhao, Xin Wei, Xiaojie Yuan
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Entity Linking, Entity Typing, Vocal Bursts Type Prediction

Entity linking, which aims at aligning ambiguous entity mentions to their referent entities in a knowledge base, plays a key role in multiple natural language processing tasks. Recently, zero-shot entity linking task has become a research hotspot, which links mentions to unseen entities to challenge the generalization ability. For this task, the training set and test set are from different domains, and thus entity linking models tend to be overfitting due to the tendency of memorizing the properties of entities that appear frequently in the training set. We argue that general ultra-fine-grained type information can help the linking models to learn contextual commonality and improve their generalization ability to tackle the overfitting problem. However, in the zero-shot entity linking setting, any type information is not available and entities are only identified by textual descriptions. Thus, we first extract the ultra-fine entity type information from the entity textual descriptions. Then, we propose a hierarchical multi-task model to improve the high-level zero-shot entity linking candidate generation task by utilizing the entity typing task as an auxiliary low-level task, which introduces extracted ultra-fine type information into the candidate generation task. Experimental results demonstrate the effectiveness of utilizing the ultra-fine entity type information and our proposed method achieves state-of-the-art performance.
Aspect-based Sentiment Analysis as Machine Reading Comprehension
Authors: Yifei Yang, Hai Zhao
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Machine Reading Comprehension, Prompt Learning, Reading Comprehension, Sentiment Analysis

Existing studies typically handle aspect-based sentiment analysis by stacking multiple neural modules, which inevitably result in severe error propagation. Instead, we propose a novel end-to-end framework, MRCOOL: MRC-PrOmpt mOdeL framework, where numerous sentiment aspects are elicited by a machine reading comprehension (MRC) model and their corresponding sentiment polarities are classified in a prompt learning way. Experiments show that our end-to-end framework consistently yields promising results on widely-used benchmark datasets which significantly outperform existing state-of-the-art models or achieve comparable performance.
Nested Named Entity Recognition as Corpus Aware Holistic Structure Parsing
Authors: Yifei Yang, Zuchao Li, Hai Zhao
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Domain Adaptation, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Nested Named Entity Recognition, Sentence

As a fundamental natural language processing task and one of core knowledge extraction techniques, named entity recognition (NER) is widely used to extract information from texts for downstream tasks. Nested NER is a branch of NER in which the named entities (NEs) are nested with each other. However, most of the previous studies on nested NER usually apply linear structure to model the nested NEs which are actually accommodated in a hierarchical structure. Thus in order to address this mismatch, this work models the full nested NEs in a sentence as a holistic structure, then we propose a holistic structure parsing algorithm to disclose the entire NEs once for all. Besides, there is no research on applying corpus-level information to NER currently. To make up for the loss of this information, we introduce Point-wise Mutual Information (PMI) and other frequency features from corpus-aware statistics for even better performance by holistic modeling from sentence-level to corpus-level. Experiments show that our model yields promising results on widely-used benchmarks which approach or even achieve state-of-the-art. Further empirical studies show that our proposed corpus-aware features can substantially improve NER domain adaptation, which demonstrates the surprising advantage of our proposed corpus-level holistic structure modeling.
DESED: Dialogue-based Explanation for Sentence-level Event Detection
Authors: Yinyi Wei, Shuaipeng Liu, Jianwei Lv, Xiangyu Xi, Hailei Yan, Wei Ye, Tong Mo, Fan Yang, Guanglu Wan
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Dialogue Generation, Event Detection, Sentence

Many recent sentence-level event detection efforts focus on enriching sentence semantics, e.g., via multi-task or prompt-based learning. Despite the promising performance, these methods commonly depend on label-extensive manual annotations or require domain expertise to design sophisticated templates and rules. This paper proposes a new paradigm, named dialogue-based explanation, to enhance sentence semantics for event detection. By saying dialogue-based explanation of an event, we mean explaining it through a consistent information-intensive dialogue, with the original event description as the start utterance. We propose three simple dialogue generation methods, whose outputs are then fed into a hybrid attention mechanism to characterize the complementary event semantics. Extensive experimental results on two event detection datasets verify the effectiveness of our method and suggest promising research opportunities in the dialogue-based explanation paradigm.
Data Augmentation for Few-Shot Knowledge Graph Completion from Hierarchical Perspective
Authors: Yuanzhou Yao, Zhao Zhang, Yongjun Xu, Chao Li
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Data Augmentation, Knowledge Graph Completion, Knowledge Graphs

Few-shot knowledge graph completion (FKGC) has become a new research focus in the field of knowledge graphs in recent years, which aims to predict the missing links for relations that only have a few associative triples. Existing models attempt to solve the problem via learning entity and relation representations. However, the limited training data severely hinders the performance of existing models. To this end, we propose to solve the FKGC problem with the data augmentation technique. Specifically, we perform data augmentation from two perspectives, i.e., inter-task view and intra-task view. The former generates new tasks for FKGC, while the latter enriches the support or query set for an individual task. It is worth noting that the proposed framework can be applied to a number of existing FKGC models. Experimental evaluation on two public datasets indicates our model is capable of achieving substantial improvements over baselines.
CLIO: Role-interactive Multi-event Head Attention Network for Document-level Event Extraction
Authors: Yubing Ren, Yanan Cao, Fang Fang, Ping Guo, Zheng Lin, Wei Ma, Yi Liu
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Document-level Event Extraction, Event Extraction

Transforming the large amounts of unstructured text on the Internet into structured event knowledge is a critical, yet unsolved goal of NLP, especially when addressing document-level text. Existing methods struggle in Document-level Event Extraction (DEE) due to its two intrinsic challenges: (a) Nested arguments, which means one argument is the sub-string of another one. (b) Multiple events, which indicates we should identify multiple events and assemble the arguments for them. In this paper, we propose a role-interactive multi-event head attention network (CLIO) to solve these two challenges jointly. The key idea is to map different events to multiple subspaces (i.e. multi-event head). In each event subspace, we draw the semantic representation of each role closer to its corresponding arguments, then we determine whether the current event exists. To further optimize event representation, we propose an event representation enhancing strategy to regularize pre-trained embedding space to be more isotropic. Our experiments on two widely used DEE datasets show that CLIO achieves consistent improvements over previous methods.
COPNER: Contrastive Learning with Prompt Guiding for Few-shot Named Entity Recognition
Authors: YuCheng Huang, Kai He, Yige Wang, Xianli Zhang, Tieliang Gong, Rui Mao, Chen Li
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Contrastive Learning, few-shot-ner, Few-shot NER, Metric Learning, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Semantic Similarity, Semantic Textual Similarity

Distance metric learning has become a popular solution for few-shot Named Entity Recognition (NER). The typical setup aims to learn a similarity metric for measuring the semantic similarity between test samples and referents, where each referent represents an entity class. The effect of this setup may, however, be compromised for two reasons. First, there is typically a limited optimization exerted on the representations of entity tokens after initing by pre-trained language models. Second, the referents may be far from representing corresponding entity classes due to the label scarcity in the few-shot setting. To address these challenges, we propose a novel approach named COntrastive learning with Prompt guiding for few-shot NER (COPNER). We introduce a novel prompt composed of class-specific words to COPNER to serve as 1) supervision signals for conducting contrastive learning to optimize token representations; 2) metric referents for distance-metric inference on test samples. Experimental results demonstrate that COPNER outperforms state-of-the-art models with a significant margin in most cases. Moreover, COPNER shows great potential in the zero-shot setting.