Thailand

In the field of dialogue summarization, due to the lack of training data, it is often difficult for supervised summary generation methods to learn vital information from dialogue context with limited data. Several attempts on unsupervised summarization for text by leveraging semantic information solely or auto-encoder strategy (i.e., sentence compression), it however cannot be adapted to the dialogue scene due to the limited words in utterances and huge gap between the dialogue and its summary. In this study, we propose a novel unsupervised strategy to address this challenge, which roots from the hypothetical foundation that a superior summary approximates a replacement of the original dialogue, and they are roughly equivalent for auxiliary (self-supervised) tasks, e.g., dialogue generation. The proposed strategy RepSum is applied to generate both extractive and abstractive summary with the guidance of the followed n^{th} utterance generation and classification tasks. Extensive experiments on various datasets demonstrate the superiority of the proposed model compared with the state-of-the-art methods.

IJCNLP-AACL 2021

RepSum: Unsupervised Dialogue Summarization based on Replacement Strategy

replacement

dialogue summarization

unsupervised

**Welcome to ACL-IJCNLP 2021!**

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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).

poster

This paper explores the task of Difficulty-Controllable Question Generation (DCQG), which aims at generating questions with required difficulty levels. Previous research on this task mainly defines the difficulty of a question as whether it can be correctly answered by a Question Answering (QA) system, lacking interpretability and controllability. In our work, we redefine question difficulty as the number of inference steps required to answer it and argue that Question Generation (QG) systems should have stronger control over the logic of generated questions. To this end, we propose a novel framework that progressively increases question difficulty through step-by-step rewriting under the guidance of an extracted reasoning chain. A dataset is automatically constructed to facilitate the research, on which extensive experiments are conducted to test the performance of our method.

Guiding the Growth: Difficulty-Controllable Question Generation through Step-by-Step Rewriting

The multimodality problem has become a major challenge of existing non-autoregressive generation (NAG) systems. A common solution often resorts to sequence-level knowledge distillation by rebuilding the training dataset through autoregressive generation (hereinafter known as ``teacher AG"). The success of such methods may largely depend on a latent assumption, i.e., the teacher AG is superior to the NAG model. However, in this work, we experimentally reveal that this assumption does not always hold for the text generation tasks like text summarization and story ending generation. To provide a feasible solution to the multimodality problem of NAG, we propose incorporating linguistic structure (Part-of-Speech sequence in particular) into NAG inference instead of relying on teacher AG. More specifically, the proposed POS-constrained Parallel Decoding (POSPD) method aims at providing a specific POS sequence to constrain the NAG model during decoding. Our experiments demonstrate that POSPD consistently improves NAG models on four text generation tasks to a greater extent compared to knowledge distillation. This observation validates the necessity of exploring the alternatives for sequence-level knowledge distillation.

POS-Constrained Parallel Decoding for Non-autoregressive Generation

In order to deeply understand the capability of pretrained language models in text generation and conduct a diagnostic evaluation, we propose TGEA, an error-annotated dataset with multiple benchmark tasks for text generation from pretrained language models (PLMs). We use carefully selected prompt words to guide GPT-2 to generate candidate sentences, from which we select 47K for error annotation. Crowdsourced workers manually check each of these sentences and detect 12k erroneous sentences. We create an error taxonomy to cover 24 types of errors occurring in these erroneous sentences according to the nature of errors with respect to linguistics and knowledge (e.g., common sense). For each erroneous span in PLM-generated sentences, we also detect another span that is closely associated with it. Each error is hence manually labeled with comprehensive annotations, including the span of the error, the associated span, minimal correction to the error, the type of the error, and rationale behind the error. Apart from the fully annotated dataset, we also present a detailed description of the data collection procedure, statistics and analysis of the dataset. This is the first dataset with comprehensive annotations for PLM-generated texts, which facilitates the diagnostic evaluation of PLM-based text generation. Furthermore, we use TGEA as a benchmark dataset and propose a series of automatic diagnosis tasks, including error detection, error type classification, associated span detection, error rationale generation, to further promote future study on the automatic error detection and correction on texts generated by pretrained language models.

TGEA: An Error-Annotated Dataset and Benchmark Tasks for TextGeneration from Pretrained Language Models

Recently, question answering (QA) based on machine reading comprehension has become popular. This work focuses on generative QA which aims to generate an abstractive answer to a given question instead of extracting an answer span from a provided passage. Generative QA often suffers from two critical problems: (1) summarizing content irrelevant to a given question, (2) drifting away from a correct answer during generation.

In this paper, we address these problems by a novel Rationale-Enriched Answer Generator (REAG), which incorporates an extractive mechanism into a generative model. Specifically, we add an extraction task on the encoder to obtain the rationale for an answer, which is the most relevant piece of text in an input document to a given question. Based on the extracted rationale and original input, the decoder is expected to generate an answer with high confidence. We jointly train REAG on the MS MARCO QA+NLG task and the experimental results show that REAG improves the quality and semantic accuracy of answers over baseline models.

Addressing Semantic Drift in Generative Question Answering with Auxiliary Extraction

This paper describes our system ECNU_ICA_1 for SemEval-2021 Task 4: Reading Comprehension of Abstract Meaning. For this task, we utilize knowledge-enhanced Graph Attention Networks with a novel semantic space transformation strategy. It leverages heterogeneous knowledge to learn adequate evidences, and seeks for an effective semantic space of abstract concepts to better improve the ability of a machine in understanding abstract meanings of natural language. Experimental results show that our system achieves strong performance on this task in terms of both imperceptibility and nonspecificity.

ECNU_ICA_1 at SemEval-2021 Task 4: Knowledge-Enhanced Graph Attention Networks for Reading Comprehension of Abstract Meaning

Given a fill-in-the-blank-type question and a corresponding context, the task is to predict the most suitable word from a list of 5 options. 
Our approaches for subtasks:
- Imperceptibility (subtask-I) - Linguistic Feature Engineering
- Non-Specificity (subtask-II) - Information from hypernyms and hyponyms from WordNet and Augmentation.
- Transformers-based models with MLM task to build our Fill-in-the-blank (FitB) models.
- Two auxiliary approaches: Chunk Voting and Max Context, to take care of input length restrictions for BERT, etc.

Improving Reading Comprehension with Abstract Words using Augmentation, Linguistic Features and Voting

This paper describes our systems for negation detection and time expression recognition in SemEval 2021 Task 10, Source-Free Domain Adaptation for Semantic Processing. We show that self-training, active learning and data augmentation techniques can improve the generalization ability of the model on the unlabeled target domain data without accessing source domain data. We also perform detailed ablation studies and error analyses for our time expression recognition systems to identify the source of the performance improvement and give constructive feedback on the temporal normalization annotation guidelines.

The University of Arizona at SemEval-2021 Task 10: Applying Self-training, Active Learning and Data Augmentation to Source-free Domain Adaptation

Scientific documents are replete with measurements mentioned in various formats and styles. As such, in a document with multiple quantities and measured entities, the task of associating each quantity to its corresponding measured entity is challenging. Thus, it is necessary to have a method to efficiently extract all measurements and attributes related to them. To this end, in this paper, we propose a novel model for the task of measurement relation extraction (MRE) whose goal is to recognize the relation between measured entities, quantities and conditions mentioned in a document. Our model employs a deep translation-based architecture to dynamically induce the important words in the document to classify the relation between a pair of entities. Furthermore, we introduce a novel regularization technique based on Information Bottleneck (IB) to filter out the noisy information from the induced set of important words. Our experiments on the recent SemEval 2021 Task 8 datasets reveal the effectiveness of the proposed model.

DPR at SemEval-2021 Task 8: Dynamic Path Reasoning for Measurement Relation Extraction

In this paper, we present our contribution in SemEval-2021 Task 1: Lexical Complexity Prediction, where we integrate linguistic, statistical, and semantic properties of the target word and its context as features within a Machine Learning (ML) framework for predicting lexical complexity.
In particular, we use BERT contextualized word embeddings to represent the semantic meaning of the target word and its context. We participated in the sub-task of predicting the complexity score of single words.

Context-aware Representation for Lexical Complexity Prediction

Detecting spans and classifying used manipulative techniques in those spans by means of the BART EncoderDecoder model to generate a marked up version of the input. How is it done, what are the limitations, and how can these be remedied.

RepSum: Unsupervised Dialogue Summarization based on Replacement Strategy

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Guiding the Growth: Difficulty-Controllable Question Generation through Step-by-Step Rewriting

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