Thailand

Document-level Relation Extraction (RE) is a more challenging task than sentence RE as it often requires reasoning over multiple sentences. Yet, human annotators usually use a small number of sentences to identify the relationship between a given entity pair. In this paper, we present an embarrassingly simple but effective method to heuristically select evidence sentences for document-level RE, which can be easily combined with BiLSTM to achieve good performance on benchmark datasets, even better than fancy graph neural network based methods. We have released our code at https://github.com/AndrewZhe/Three-Sentences-Are-All-You-Need.

ACL-IJCNLP 2021

Three Sentences Are All You Need: Local Path Enhanced Document Relation Extraction

document relation extraction

interpretability

information extraction

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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).

Few-shot relation extraction (FSRE) is of great importance in long-tail distribution problem, especially in special domain with low-resource data. Most existing FSRE algorithms fail to accurately classify the relations merely based on the information of the sentences together with the recognized entity pairs, due to limited samples and lack of knowledge. To address this problem, in this paper, we proposed a novel entity CONCEPT-enhanced FEw-shot Relation Extraction scheme (ConceptFERE), which introduces the inherent concepts of entities to provide clues for relation prediction and boost the relations classification performance. Firstly, a concept-sentence attention module is developed to select the most appropriate concept from multiple concepts of each entity by calculating the semantic similarity between sentences and concepts. Secondly, a self-attention based fusion module is presented to bridge the gap of concept embedding and sentence embedding from different semantic spaces. Extensive experiments on the FSRE benchmark dataset FewRel have demonstrated the effectiveness and the superiority of the proposed ConceptFERE scheme as compared to the state-of-the-art baselines. Code is available at https://github.com/LittleGuoKe/ConceptFERE.

Entity Concept-enhanced Few-shot Relation Extraction

Biomedical Information Extraction from scientific literature presents two unique and non-trivial challenges.
First, compared with general natural language texts, sentences from scientific papers usually possess wider contexts between knowledge elements. Moreover, comprehending the fine-grained scientific entities and events urgently requires domain-specific background knowledge. In this paper, we propose a novel biomedical Information Extraction (IE) model to tackle these two challenges and extract scientific entities and events from English research papers. We perform Abstract Meaning Representation (AMR) to compress the wide context to uncover a clear semantic structure for each complex sentence. Besides, we construct the sentence-level knowledge graph from an external knowledge base and use it to enrich the AMR graph to improve the model's understanding of complex scientific concepts. We use an edge-conditioned graph attention network to encode the knowledge-enriched AMR graph for biomedical IE tasks.
Experiments on the GENIA 2011 dataset show that the AMR and external knowledge have contributed 1.8% and 3.0% absolute F-score gains respectively. In order to evaluate the impact of our approach on real-world problems that involve topic-specific fine-grained knowledge elements, we have also created a new ontology and annotated corpus for entity and event extraction for the COVID-19 scientific literature, which can serve as a new benchmark for the biomedical IE community.

Fine-grained Information Extraction from Biomedical Literature based on Knowledge-enriched Abstract Meaning Representation

Event Detection (ED) aims to recognize mentions of events (i.e., event triggers) and their types in text. Recently, several ED datasets in various domains have been proposed. However, the major limitation of these resources is the lack of enough training data for individual event types which hinders the efficient training of data-hungry deep learning models. To overcome this issue, we propose to exploit the powerful pre-trained language model GPT-2 to generate training samples for ED. To prevent the noises inevitable in automatically generated data from hampering training process, we propose to exploit a teacher-student architecture in which the teacher is supposed to learn anchor knowledge from the original data. The student is then trained on combination of the original and GPT-generated data while being led by the anchor knowledge from the teacher. Optimal transport is introduced to facilitate the anchor knowledge-based guidance between the two networks. We evaluate the proposed model on multiple ED benchmark datasets, gaining consistent improvement and establishing state-of-the-art results for ED.

Unleash GPT-2 Power for Event Detection

Generalization is an important ability that helps to ensure that a machine learning model can perform well on unseen data. In this paper, we study the effect of data bias on model generalization, using Chinese Named Entity Recognition (NER) as a case study. Specifically, we analyzed five benchmarking datasets for Chinese NER, and observed the following two types of data bias that can compromise model generalization ability. Firstly, the test sets of all the five datasets contain a significant proportion of entities that have been seen in the training sets. Such test data would therefore not be able to reflect the true generalization ability of a model. Secondly, all datasets are dominated by a few fat-head entities, i.e., entities appearing with particularly high frequency. As a result, a model might be able to produce high prediction accuracy simply by keyword memorization without leveraging context knowledge. To address these data biases, we first refine each test set by excluding seen entities from it, so as to better evaluate a model's generalization ability. Then, we propose a simple yet effective entity resampling method to make entities within the same category distributed equally, encouraging a model to leverage both name and context knowledge in the training process. Experimental results demonstrate that the proposed entity resampling method significantly improves a model’s ability in detecting unseen entities, especially for company, organization and position categories.

Improving Model Generalization: A Chinese Named Entity Recognition Case Study

Event extraction (EE) has considerably benefited from pre-trained language models (PLMs) by fine-tuning. However, existing pre-training methods have not involved modeling event characteristics, resulting in the developed EE models cannot take full advantage of large-scale unsupervised data. To this end, we propose CLEVE, a contrastive pre-training framework for EE to better learn event knowledge from large unsupervised data and their semantic structures (e.g. AMR) obtained with automatic parsers. CLEVE contains a text encoder to learn event semantics and a graph encoder to learn event structures respectively. Specifically, the text encoder learns event semantic representations by self-supervised contrastive learning to represent the words of the same events closer than those unrelated words; the graph encoder learns event structure representations by graph contrastive pre-training on parsed event-related semantic structures. The two complementary representations then work together to improve both the conventional supervised EE and the unsupervised ``liberal'' EE, which requires jointly extracting events and discovering event schemata without any annotated data. Experiments on ACE 2005 and MAVEN datasets show that CLEVE achieves significant improvements, especially in the challenging unsupervised setting. The source code and pre-trained checkpoints can be obtained from https://github.com/THU-KEG/CLEVE.

CLEVE: Contrastive Pre-training for Event Extraction

Disagreement between coders is ubiquitous in virtually all datasets annotated with human judgements in both natural language processing and computer vision. However, most supervised machine learning methods assumethat a single preferred interpretation exists for each item, which is at best an idealization. The aim of the SemEval-2021 shared task on Learning with Disagreements (Le-wi-Di) wasto provide a unified testing framework for methods for learning from data containing multiple and possibly contradictory annotations covering the best-known datasets containing information about disagreements for interpreting language and classifying images. In this paper we describe the shared task and its results.

SemEval 2021 Task 12: Learning with Disagreement

This paper describes our system participated in Task 6 of SemEval-2021: the task is focused on multimodal propaganda technique classification and it aims to classify given image and text into 22 classes. In this paper, a multi-model architecture for learning representations by using a Transformer encoder to fusion text and vision features is proposed. To increase the diversity of the system, we have tested both grid and region features for the visual input. Furthermore, ERNIE-ViL, a transformer-based model specifically designed for multi-mode, is applied to produce a better representation for both text and image. Besides, as the distribution of the classification labels is very unbalanced, we also make a further attempt on the evaluation function and the experiment result shows that focal loss would perform better than cross entropy loss. Last we have won first for subtask C in the final competition.

Alpha at SemEval-2021 Tasks 6: Transformer Based Propaganda Classification

Radiology reports contain important clinical information that can be used to automatically construct fine-grained labels for applications requiring deep phenotyping. We propose a two-turn question answering (QA) method based on a transformer language model, BERT, for extracting detailed spatial information from radiology reports. Our proposed method identifies spatial and descriptor information by answering queries given a radiology report text. We frame the extraction problem such that all the main radiology entities (e.g., find- ing, device, anatomy) and the spatial trigger terms (denoting the presence of a spatial relation between finding/device and anatomical location) are identified in the first turn. In the subsequent turn, various other contextual information that acts as important spatial roles with respect to a spatial trigger term are extracted along with identifying the spatial and other descriptor terms qualifying a radiological entity. The queries are constructed using separate templates for the two turns and we employ two query variations in the second turn. When compared to the best-reported work on this task using a traditional sequence tagging method, the two-turn QA model exceeds its performance on every component. This includes promising improvements of 12, 13, and 12 points in the average F1 scores for identifying the spatial triggers, Figure, and Ground frame elements, respectively.

Fine-Grained Spatial Information Extraction in Radiology as Two-turn Question Answering

In this paper, we define and evaluate a methodology for extracting history-dependent spatial questions from visual dialogues. We say that a question is history-dependent if it requires (parts of) their dialogue history to be interpreted. We argue that some kinds of visual questions define a context upon which a follow-up spatial question relies. We call the question that restricts the context: trigger, and we call the spatial question that requires the trigger question to be answered: zoomer. We automatically extract different trigger and zoomer pairs based on the visual property that the questions rely on (e.g. color, number). We manually annotate the automatically extracted trigger and zoomer pairs to verify which zoomers require their trigger. We implement a simple baseline architecture based on a SOTA multimodal encoder. It leaves much room for improvement for answering history-dependent questions

Visually Grounded Follow-up Questions: a Dataset of Spatial Questions Which Require Dialogue History

How pre-trained transformer-based language models perform grounded language acquisition through cross-situational learning (CSL) remains unclear.
In particular, it is still not understood how meaning concepts are captured from complex sentences, along with learning language-based interactions,
could benefit the field of human-robot interactions and help understand how children learn and ground language.
In our current work, we study cross-situational learning to understand the mechanisms enabling children to learn rapidly sequence of words to meaning concepts: the two sequence-based models (i) Echo State Networks (i.e., Reservoir Computing), and (ii) Long-Short Term Memory Networks (LSTM) makes this mapping.
We consider the three different input representations: (i) One-Hot encoding, (ii) BERT fine-tuned on Juven+GOLD corpus, and (iii) Google BERT.
We investigate which of these three representations better predict the stimulated vision as a function of sentences describing the scenes using two models.
Using our approach, we test two datasets: Juven and GoLD, and present how these models generalize only after a few hundred partially described scenes via cross-situational learning.
We find that both One-Hot encoding and BERT fine-tuned representations (for both models) significantly improve the predictions.
Moreover, we argue that these models are able to learn complex relations between the contexts in which a word appears and their corresponding meaning concepts, handling polysemous and synonymous words.
This aspect could be incorporated into a human-robot interaction study that examines grounding language to objects in a physical world and poses a challenge for researchers to investigate better the use of transformer models in robotics and HCI.

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