Korea, Republic of

Event extraction (EE) is an essential task of information extraction, which aims to extract structured event information from unstructured text. Most prior work focuses on extracting flat events while neglecting overlapped or nested ones. A few models for overlapped and nested EE includes several successive stages to extract event triggers and arguments, which suffer from error propagation. Therefore, we design a simple yet effective tagging scheme and model to formulate EE as word-word relation recognition, called OneEE. The relations between trigger or argument words are simultaneously recognized in one stage with parallel grid tagging, thus yielding a very fast event extraction speed. The model is equipped with an adaptive event fusion module to generate event-aware representations and a distance-aware predictor to integrate relative distance information for wordword relation recognition, which are empirically demonstrated to be effective mechanisms. Experiments on 3 overlapped and nested EE benchmarks, namely FewFC, Genia11, and Genia13, show that OneEE achieves the stateof-the-art (SoTA) results. Moreover, the inference speed of OneEE is faster than those of baselines in the same condition, and can be further substantially improved since it supports parallel inference.


COLING 2022

OneEE: A One-Stage Framework for Fast Overlapping and Nested Event Extraction

one stage.

overlapping and nested ee

event extraction

**Welcome to EMNLP 2023!**

On behalf of the EMNLP 2023 Organizing Committee, I extend a warm and heartfelt welcome to all of you to the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP). It is with immense pleasure and excitement that we gather here in Singapore, a vibrant hub of innovation and technological advancement.

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EMNLP 2023

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technical paper

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COLING, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

Event argument extraction (EAE) aims to extract arguments with given roles from texts, which have been widely studied in natural language processing. Most previous works have achieved good performance in specific EAE datasets with dedicated neural architectures. Whereas, these architectures are usually difficult to adapt to new datasets/scenarios with various annotation schemas or formats. Furthermore, they rely on large-scale labeled data for training, which is unavailable due to the high labelling cost in most cases. In this paper, we propose a multi-format transfer learning model with variational information bottleneck, which makes use of the information especially the common knowledge in existing datasets for EAE in new datasets. Specifically, we introduce a shared-specific prompt framework to learn both format-shared and format-specific knowledge from datasets with different formats. In order to further absorb the common knowledge for EAE and eliminate the irrelevant noise, we integrate variational information bottleneck into our architecture to refine the shared representation. We conduct extensive experiments on three benchmark datasets, and obtain new state-of-the-art performance on EAE.

A Multi-Format Transfer Learning Model for Event Argument Extraction via Variational Information Bottleneck

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 webtable 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.

A Relation Extraction Dataset for Knowledge Extraction from Web Tables

As AI is more and more pervasive in everyday life, humans have an increasing demand to understand its behavior and decisions. Most research on explainable AI builds on the premise that there is one ideal explanation to be found. In fact, however, everyday explanations are co-constructed in a dialogue between the person explaining (the explainer) and the specific person being explained to (the explainee). In this paper, we introduce a first corpus of dialogical explanations to enable NLP research on how humans explain as well as on how AI can learn to imitate this process. The corpus consists of 65 transcribed English dialogues from the Wired video series 5 Levels, explaining 13 topics to five explainees of different proficiency. All 1550 dialogue turns have been manually labeled by five independent professionals for the topic discussed as well as for the dialogue act and the explanation move performed. We analyze linguistic patterns of explainers and explainees, and we explore differences across proficiency levels. BERT-based baseline results indicate that sequence information helps predicting topics, acts, and moves effectively.

"Mama Always Had a Way of Explaining Things So I Could Understand": A Dialogue Corpus for Learning to Construct Explanations

In contrast to traditional exhaustive search, selective search first clusters documents into several groups before all the documents are searched exhaustively by a query, to limit the search executed within one group or only a few groups. Selective search is designed to reduce the latency and computation in modern large-scale search systems. In this study, we propose MICO, a \textbf{M}utual \textbf{I}nformation \textbf{CO}-training framework for selective search with minimal supervision using the search logs. After training, MICO does not only cluster the documents, but also routes unseen queries to the relevant clusters for efficient retrieval. In our empirical experiments, MICO significantly improves the performance on multiple metrics of selective search and outperforms a number of existing competitive baselines.

MICO: Selective Search with Mutual Information Co-training

We propose a biomedical knowledge enhanced pre-trained language model for medicinal product vertical search. We present a novel pre-training task, product attribute prediction (PAP), to incorporate medicinal product knowledge into the pre-trained language model. Experiments demonstrate the effectiveness of PAP pre-training task for medicinal product vertical search.

A Domain Knowledge Enhanced Pre-Trained Language Model for Vertical Search: Case Study on Medicinal Products

Table-based fact verification aims to verify whether a statement sentence is trusted or fake. Most existing methods rely on graph features or data augmentation but fail to investigate evidence correlation between the statement and table effectively. We propose a self-Labeled Keypoint Alignment model to explore the correlation between the two. Specifically, a dual-view alignment module based on statement and table views is designed to discriminate the salient words through multiple interactions. A novel mixture-of-experts block is designed to integrate the interacted information for supporting the alignment and final classification. A contrastive learning loss is utilized to learn the precise representation of the structure-involved words.

Table-based fact verification with self-labeled keypoint alignment

Graph neural networks (GNNs) have been recently applied in natural language processing. Various GNN research studies are proposed to learn node interactions within the local graph of each document that contains words, sentences, or topics for inductive text classification. However, most inductive GNNs that are built on a word graph generally take global word embeddings as node features, without referring to
document-wise contextual information. Consequently, we find that BERT models can perform better than inductive GNNs. An intuitive follow-up approach is to enrich GNNs with contextual embeddings from BERT, yet there is a lack of related research. In this work, we propose a simple yet effective unified model, coined ConTextING, with a joint training mechanism to learn from both document embeddings and contextual word interactions simultaneously. Our experiments show that ConTextING outperforms pure inductive GNNs and BERT-style models. The analyses also highlight the benefits of the sub-word graph and joint training with separated classifiers.

ConTextING: Granting Document-Wise Contextual Embeddings to Graph Neural Networks for Inductive Text Classification

Recent years have witnessed the proliferation of offensive content online such as fake news, propaganda, misinformation, and disinformation. While initially this was mostly about textual content, over time images and videos gained popularity, as they are much easier to consume, attract more attention, and spread further than text. As a result, researchers started leveraging different modalities and combinations thereof to tackle online multimodal offensive content. In this study, we offer a survey on the state-of-the-art on multimodal disinformation detection covering various combinations of modalities: text, images, speech, video, social media network structure, and temporal information. Moreover, while some studies focused on {\em factuality}, others investigated how harmful the content is. While these two components in the definition of disinformation -- (i) factuality, and (ii)harmfulness --, are equally important, they are typically studied in isolation. Thus, we argue for the need to tackle disinformation detection by taking into account multiple modalities as well as both factuality and harmfulness, in the same framework. Finally, we discuss current challenges and future research directions.

A Survey on Multimodal Disinformation Detection

KGE-CL: Contrastive Learning of Tensor Decomposition Based Knowledge Graph Embeddings

Recent studies show that NLP models trained on standard English texts tend to produce biased outcomes against underrepresented English varieties. In this work, we conduct a pioneering study of the English variety use of African American English (AAE) in NLI task. First, we propose CODESWITCH, a greedy unidirectional morphosyntactically-informed rule-based translation method for data augmentation. Next, we use CODESWITCH to present a preliminary study to determine if demographic language features do in fact influence models to produce false predictions. Then, we conduct experiments on two popular datasets and propose two simple, yet effective and generalizable debiasing methods. Our findings show that NLI models (e.g. BERT) trained under our proposed frameworks outperform traditional large language models while maintaining or even improving the prediction performance. In addition, we intend to release CODESWITCH, in hopes of promoting dialectal language diversity in training data to both reduce the discriminatory societal impacts and improve model robustness of downstream NLP tasks.

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A Multi-Format Transfer Learning Model for Event Argument Extraction via Variational Information Bottleneck

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