Thailand

Event Detection (ED) aims to identify event trigger words from a given text and classify it into an event type. 
Most current methods to ED rely heavily on training instances, and almost ignore the correlation of event types. Hence, they tend to suffer from data scarcity and fail to handle new unseen event types. 
To address these problems, we formulate ED as a process of event ontology population: linking event instances to pre-defined event types in event ontology, and propose a novel ED framework entitled OntoED with ontology embedding. 
We enrich event ontology with linkages among event types, and further induce more event-event correlations. 
Based on the event ontology, OntoED can leverage and propagate correlation knowledge, particularly from data-rich to data-poor event types. 
Furthermore, OntoED can be applied to new unseen event types, by establishing linkages to existing ones. 
Experiments indicate that OntoED is more predominant and robust than previous approaches to ED, especially in data-scarce scenarios.

ACL-IJCNLP 2021

OntoED: Low-resource Event Detection with Ontology Embedding

ontology embedding

event detection

low-resource

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technical paper

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The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

Entity Matching (EM) aims at recognizing entity records that denote the same real-world object. Neural EM models learn vector representation of entity descriptions and match entities end-to-end. Though robust, these methods require many annotated resources for training, and lack of interpretability. In this paper, we propose a novel EM framework that consists of Heterogeneous Information Fusion (HIF) and Key Attribute Tree (KAT) Induction to decouple feature representation from matching decision. Using self-supervised learning and mask mechanism in pre-trained language modeling, HIF learns the embeddings of noisy attribute values by inter-attribute attention with unlabeled data. Using a set of comparison features and a limited amount of annotated data, KAT Induction learns an efficient decision tree that can be interpreted by generating entity matching rules whose structure is advocated by domain experts. Experiments on 6 public datasets and 3 industrial datasets show that our method is highly efficient and outperforms SOTA EM models in most cases. We will release the codes upon acceptance.

Interpretable and Low-Resource Entity Matching via Decoupling Feature Learning from Decision Making

Named entity recognition (NER) is a well-studied task in natural language processing. Traditional NER research only deals with flat entities and ignores nested entities. The span-based methods treat entity recognition as a span classification task. Although these methods have the innate ability to handle nested NER, they suffer from high computational cost, ignorance of boundary information, under-utilization of the spans that partially match with entities, and difficulties in long entity recognition. To tackle these issues, we propose a two-stage entity identifier. First we generate span proposals by filtering and boundary regression on the seed spans to locate the entities, and then label the boundary-adjusted span proposals with the corresponding categories. Our method effectively utilizes the boundary information of entities and partially matched spans during training. Through boundary regression, entities of any length can be covered theoretically, which improves the ability to recognize long entities. In addition, many low-quality seed spans are filtered out in the first stage, which reduces the time complexity of inference. Experiments on nested NER datasets demonstrate that our proposed method outperforms previous state-of-the-art models.

Locate and Label: A Two-stage Identifier for Nested Named Entity Recognition

Event extraction is challenging due to the complex structure of event records and the semantic gap between text and event.
Traditional methods usually extract event records by decomposing the complex structure prediction task into multiple subtasks.
In this paper, we propose Text2Event, a sequence-to-structure generation paradigm that can directly extract events from the text in an end-to-end manner.
Specifically, we design a sequence-to-structure network for unified event extraction, a constrained decoding algorithm for event knowledge injection during inference, and a curriculum learning algorithm for efficient model learning. 
Experimental results show that, by uniformly modeling all tasks in a single model and universally predicting different labels, our method can achieve competitive performance using only record-level annotations in both supervised learning and transfer learning settings.

Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction

In this paper, we aim to explore an uncharted territory, which is Chinese multimodal named entity recognition (NER) with both textual and acoustic contents. To achieve this, we construct a large-scale human-annotated Chinese multimodal NER dataset, named \texttt{CNERTA}. Our corpus totally contains 42,987 annotated sentences accompanying by 71 hours of speech data. Based on this dataset, we propose a family of strong and representative baseline models, which can leverage textual features or multimodal features. Upon these baselines, to capture the natural monotonic alignment between the textual modality and the acoustic modality, we further propose a simple multimodal multitask model by introducing a speech-to-text alignment auxiliary task. Through extensive experiments, we observe that: (1) Progressive performance boosts as we move from unimodal to multimodal, verifying the necessity of integrating speech clues into Chinese NER. (2) Our proposed model yields state-of-the-art (SoTA) results on \texttt{CNERTA}, demonstrating its effectiveness. For further research, the annotated dataset is publicly available at \url{http://github.com/DianboWork/CNERTA}.

A Large-Scale Chinese Multimodal NER Dataset with Speech Clues

Disease is one of the fundamental entities in biomedical research. Recognizing such entities from biomedical text and then normalizing them to a standardized disease vocabulary offer a tremendous opportunity for many downstream applications. Previous studies have demonstrated that joint modeling of the two sub-tasks has superior performance than the pipelined counterpart. Although the neural joint model based on multi-task learning framework has achieved state-of-the-art performance, it suffers from the boundary inconsistency problem due to the separate decoding procedures. Moreover, it ignores the rich information (e.g., the text surface form) of each candidate concept in the vocabulary, which is quite essential for entity normalization. In this work, we propose a neural transition-based joint model to alleviate these two issues. We transform the end-to-end disease recognition and normalization task as an action sequence prediction task, which not only jointly learns the model with shared representations of the input, but also jointly searches the output by state transitions in one search space. Moreover, we introduce attention mechanisms to take advantage of the text surface form of each candidate concept for better normalization performance. Experimental results conducted on two publicly available datasets show the effectiveness of the proposed method.

A Neural Transition-based Joint Model for Disease Named Entity Recognition and Normalization

Machine Reading Comprehension (MRC) aims to extract answers to questions given a passage, 
which has been widely studied recently especially in open domains. 
However, few efforts have been made on closed-domain MRC, mainly due to the lack of large-scale training data. In this paper, we introduce a multi-target MRC task for the medical domain, whose goal is to predict answers to medical questions and the corresponding support sentences from medical information sources simultaneously, in order to ensure the high reliability of medical knowledge serving. A high-quality dataset more than 18k samples) is manually constructed for the purpose, named Multi-task Chinese Medical MRC dataset (CMedMRC), with detailed analysis conducted. We further propose a Chinese medical BERT model for the task (CMedBERT), which fuses medical knowledge into pre-trained language models by the dynamic fusion mechanism of heterogeneous features and the multi-task learning strategy.
Experiments show that CMedBERT consistently outperforms strong baselines by fusing context-aware and knowledge-aware token representations.

Knowledge-Empowered Representation Learning for Chinese Medical Reading Comprehension: Task, Model and Resources

Chinese spelling correction (CSC) is an important yet challenging task. Existing state-of-the-art methods either only use a pre-trained language model or incorporate phonological information as external knowledge. In this paper, we propose a novel end-to-end CSC model that integrates phonetic features into language model by leveraging the powerful pre-training and fine-tuning method. Instead of conventionally masking words with a special token in training language model, we replace words with phonetic features and their sound-alike words. We further propose an adaptive weighted objective to jointly train error detection and correction in a unified framework. Experimental results show that our model achieves significant improvements on SIGHAN datasets and 
outperforms the previous state-of-the-art methods.

Correcting Chinese Spelling Errors with Phonetic Pre-training

Question generation is the task of generating coherent and relevant questions given context paragraphs. Recently, with the development of large-scale question answering datasets such as SQuAD, the English question generation has been rapidly developed. However, for other languages such as Chinese, the available training data is limited, which hinders the development of question generation in the corresponding language. To investigate the multi-lingual question generation, in this paper, we develop a language-agnostic language model, which learns the shared representation from several languages in a single architecture. We propose an adversarial training objective to encourage the model to learn both language-specific and language-independent information. We utilize abundant monolingual text to improve the multi-lingual question generation via pre-training. With the language-agnostic language model, we achieve significant improvement in multi-lingual question generation over five languages. In addition, we propose a large-scale Chinese question generation dataset containing more than 220k human-generated questions to benefit the multi-lingual question generation research.

Multi-Lingual Question Generation with Language Agnostic Language Model

Pre-trained transformer language models have shown remarkable performance on a variety of NLP tasks. However, recent research has suggested that phrase-level representations in these models reflect heavy influences 
of lexical content, but lack evidence of sophisticated, compositional phrase information. Here we investigate the impact of fine-tuning on the capacity of contextualized embeddings to capture phrase meaning information beyond lexical content. Specifically, we fine-tune models on an adversarial paraphrase classification task with high lexical overlap, and on a sentiment classification task. After fine-tuning, we analyze phrasal representations in controlled settings following prior work. We find that fine-tuning largely fails to benefit compositionality in these representations, though training on sentiment yields a small, localized benefit for certain models. In follow-up analyses, we identify confounding cues in the paraphrase dataset that may explain the lack of composition benefits from that task, and we discuss factors underlying the localized benefits from sentiment training.

On the Interplay Between Fine-tuning and Composition in Transformers

Lifelong topic models mainly focus on in-domain text streams in which each chunk only contains documents from a single domain. To overcome data diversity of the in-domain corpus, most of the existing methods exploit the information from limited sources in a separate and heuristic manner. In this study, we develop a lifelong collaborative model (LCM) based on non-negative matrix factorization to accurately learn topics and domain-specific word embeddings. LCM particularly investigates: (1) developing a knowledge graph based on the semantic relationships among words in the lifelong learning process, so as to accumulate global context information discovered by topic models and local context information reflected by context word embeddings from previous domains, and (2) developing a subword graph based on byte pair encoding and pairwise word relationships to exploit subword information of words in the current in-domain corpus. To the best of our knowledge, we are the first to collaboratively learn topics and word embeddings via lifelong learning. Experiments on real-world in-domain text streams validate the effectiveness of our method.

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