pwc-archive/papers-with-abstracts huggingface dataset:
DAPPER: Learning Domain-Adapted Persona Representation Using Pretrained BERT and External Memory
Authors: Prashanth Vijayaraghavan, Eric Chu, Deb Roy
Date: 2020-12-01 00:00:00
Proceeding: Asian Chapter of the Association for Computational Linguistics 2020
Tasks: Hate Speech Detection, Language Modeling, Language Modelling

Research in building intelligent agents have emphasized the need for understanding characteristic behavior of people. In order to reflect human-like behavior, agents require the capability to comprehend the context, infer individualized persona patterns and incrementally learn from experience. In this paper, we present a model called DAPPER that can learn to embed persona from natural language and alleviate task or domain-specific data sparsity issues related to personas. To this end, we implement a text encoding strategy that leverages a pretrained language model and an external memory to produce domain-adapted persona representations. Further, we evaluate the transferability of these embeddings by simulating low-resource scenarios. Our comparative study demonstrates the capability of our method over other approaches towards learning rich transferable persona embeddings. Empirical evidence suggests that the learnt persona embeddings can be effective in downstream tasks like hate speech detection.
Event Coreference Resolution with Non-Local Information
Authors: Jing Lu, Vincent Ng
Date: 2020-12-01 00:00:00
Proceeding: Asian Chapter of the Association for Computational Linguistics 2020
Tasks: coreference-resolution, Coreference Resolution, Event Coreference Resolution

We present two extensions to a state-of-theart joint model for event coreference resolution, which involve incorporating (1) a supervised topic model for improving trigger detection by providing global context, and (2) a preprocessing module that seeks to improve event coreference by discarding unlikely candidate antecedents of an event mention using discourse contexts computed based on salient entities. The resulting model yields the best results reported to date on the KBP 2017 English and Chinese datasets.
Answering Product-related Questions with Heterogeneous Information
Authors: Wenxuan Zhang, Qian Yu, Wai Lam
Date: 2020-12-01 00:00:00
Proceeding: Asian Chapter of the Association for Computational Linguistics 2020
Tasks: Attribute, Question Answering

Providing instant response for product-related questions in E-commerce question answering platforms can greatly improve users{'} online shopping experience. However, existing product question answering (PQA) methods only consider a single information source such as user reviews and/or require large amounts of labeled data. In this paper, we propose a novel framework to tackle the PQA task via exploiting heterogeneous information including natural language text and attribute-value pairs from two information sources of the concerned product, namely product details and user reviews. A heterogeneous information encoding component is then designed for obtaining unified representations of information with different formats. The sources of the candidate snippets are also incorporated when measuring the question-snippet relevance. Moreover, the framework is trained with a specifically designed weak supervision paradigm making use of available answers in the training phase. Experiments on a real-world dataset show that our proposed framework achieves superior performance over state-of-the-art models.
Two-Step Classification using Recasted Data for Low Resource Settings
Authors: Shagun Uppal, Vivek Gupta, Avinash Swaminathan, Haimin Zhang, Debanjan Mahata, Rakesh Gosangi, Rajiv Ratn Shah, Amanda Stent
Date: 2020-12-01 00:00:00
Proceeding: Asian Chapter of the Association for Computational Linguistics 2020
Tasks: Classification, Natural Language Inference, text-classification, Text Classification, Vocal Bursts Valence Prediction

An NLP model{'}s ability to reason should be independent of language. Previous works utilize Natural Language Inference (NLI) to understand the reasoning ability of models, mostly focusing on high resource languages like English. To address scarcity of data in low-resource languages such as Hindi, we use data recasting to create NLI datasets for four existing text classification datasets. Through experiments, we show that our recasted dataset is devoid of statistical irregularities and spurious patterns. We further study the consistency in predictions of the textual entailment models and propose a consistency regulariser to remove pairwise-inconsistencies in predictions. We propose a novel two-step classification method which uses textual-entailment predictions for classification task. We further improve the performance by using a joint-objective for classification and textual entailment. We therefore highlight the benefits of data recasting and improvements on classification performance using our approach with supporting experimental results.
Explaining Word Embeddings via Disentangled Representation
Authors: Keng-Te Liao, Cheng-Syuan Lee, Zhong-Yu Huang, Shou-De Lin
Date: 2020-12-01 00:00:00
Proceeding: Asian Chapter of the Association for Computational Linguistics 2020
Tasks: Disentanglement, Word Embeddings

Disentangled representations have attracted increasing attention recently. However, how to transfer the desired properties of disentanglement to word representations is unclear. In this work, we propose to transform typical dense word vectors into disentangled embeddings featuring improved interpretability via encoding polysemous semantics separately. We also found the modular structure of our disentangled word embeddings helps generate more efficient and effective features for natural language processing tasks.
Multi-view Classification Model for Knowledge Graph Completion
Authors: Wenbin Jiang, Mengfei Guo, Yufeng Chen, Ying Li, Jinan Xu, Yajuan Lyu, Yong Zhu
Date: 2020-12-01 00:00:00
Proceeding: Asian Chapter of the Association for Computational Linguistics 2020
Tasks: Classification, Knowledge Graph Completion

Most previous work on knowledge graph completion conducted single-view prediction or calculation for candidate triple evaluation, based only on the content information of the candidate triples. This paper describes a novel multi-view classification model for knowledge graph completion, where multiple classification views are performed based on both content and context information for candidate triple evaluation. Each classification view evaluates the validity of a candidate triple from a specific viewpoint, based on the content information inside the candidate triple and the context information nearby the triple. These classification views are implemented by a unified neural network and the classification predictions are weightedly integrated to obtain the final evaluation. Experiments show that, the multi-view model brings very significant improvements over previous methods, and achieves the new state-of-the-art on two representative datasets. We believe that, the flexibility and the scalability of the multi-view classification model facilitates the introduction of additional information and resources for better performance.
Knowledge-Enhanced Named Entity Disambiguation for Short Text
Authors: Zhifan Feng, Qi Wang, Wenbin Jiang, Yajuan Lyu, Yong Zhu
Date: 2020-12-01 00:00:00
Proceeding: Asian Chapter of the Association for Computational Linguistics 2020
Tasks: Entity Disambiguation, Information Retrieval, Question Answering, Retrieval

Named entity disambiguation is an important task that plays the role of bridge between text and knowledge. However, the performance of existing methods drops dramatically for short text, which is widely used in actual application scenarios, such as information retrieval and question answering. In this work, we propose a novel knowledge-enhanced method for named entity disambiguation. Considering the problem of information ambiguity and incompleteness for short text, two kinds of knowledge, factual knowledge graph and conceptual knowledge graph, are introduced to provide additional knowledge for the semantic matching between candidate entity and mention context. Our proposed method achieves significant improvement over previous methods on a large manually annotated short-text dataset, and also achieves the state-of-the-art on three standard datasets. The short-text dataset and the proposed model will be publicly available for research use.
ExpanRL: Hierarchical Reinforcement Learning for Course Concept Expansion in MOOCs
Authors: Jifan Yu, Chenyu Wang, Gan Luo, Lei Hou, Juanzi Li, Jie Tang, Minlie Huang, Zhiyuan Liu
Date: 2020-12-01 00:00:00
Proceeding: Asian Chapter of the Association for Computational Linguistics 2020
Tasks: Diversity, Hierarchical Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Within the prosperity of Massive Open Online Courses (MOOCs), the education applications that automatically provide extracurricular knowledge for MOOC users become rising research topics. However, MOOC courses{'} diversity and rapid updates make it more challenging to find suitable new knowledge for students. In this paper, we present ExpanRL, an end-to-end hierarchical reinforcement learning (HRL) model for concept expansion in MOOCs. Employing a two-level HRL mechanism of seed selection and concept expansion, ExpanRL is more feasible to adjust the expansion strategy to find new concepts based on the students{'} feedback on expansion results. Our experiments on nine novel datasets from real MOOCs show that ExpanRL achieves significant improvements over existing methods and maintain competitive performance under different settings.
Vocabulary Matters: A Simple yet Effective Approach to Paragraph-level Question Generation
Authors: Vishwajeet Kumar, Manish Joshi, Ganesh Ramakrishnan, Yuan-Fang Li
Date: 2020-12-01 00:00:00
Proceeding: Asian Chapter of the Association for Computational Linguistics 2020
Tasks: Question Generation, Question-Generation

Question generation (QG) has recently attracted considerable attention. Most of the current neural models take as input only one or two sentences, and perform poorly when multiple sentences or complete paragraphs are given as input. However, in real-world scenarios it is very important to be able to generate high-quality questions from complete paragraphs. In this paper, we present a simple yet effective technique for answer-aware question generation from paragraphs. We augment a basic sequence-to-sequence QG model with dynamic, paragraph-specific dictionary and copy attention that is persistent across the corpus, without requiring features generated by sophisticated NLP pipelines or handcrafted rules. Our evaluation on SQuAD shows that our model significantly outperforms current state-of-the-art systems in question generation from paragraphs in both automatic and human evaluation. We achieve a 6-point improvement over the best system on BLEU-4, from 16.38 to 22.62.
From Hero to Z\'eroe: A Benchmark of Low-Level Adversarial Attacks
Authors: Steffen Eger, Yannik Benz
Date: 2020-12-01 00:00:00
Proceeding: Asian Chapter of the Association for Computational Linguistics 2020
Tasks: Natural Language Inference, Part-Of-Speech Tagging, Toxic Comment Classification

Adversarial attacks are label-preserving modifications to inputs of machine learning classifiers designed to fool machines but not humans. Natural Language Processing (NLP) has mostly focused on high-level attack scenarios such as paraphrasing input texts. We argue that these are less realistic in typical application scenarios such as in social media, and instead focus on low-level attacks on the character-level. Guided by human cognitive abilities and human robustness, we propose the first large-scale catalogue and benchmark of low-level adversarial attacks, which we dub Z{\'e}roe, encompassing nine different attack modes including visual and phonetic adversaries. We show that RoBERTa, NLP{'}s current workhorse, fails on our attacks. Our dataset provides a benchmark for testing robustness of future more human-like NLP models.
Reconstructing Event Regions for Event Extraction via Graph Attention Networks
Authors: Pei Chen, Hang Yang, Kang Liu, Ruihong Huang, Yubo Chen, Taifeng Wang, Jun Zhao
Date: 2020-12-01 00:00:00
Proceeding: Asian Chapter of the Association for Computational Linguistics 2020
Tasks: Event Extraction, Graph Attention, Sentence

Event information is usually scattered across multiple sentences within a document. The local sentence-level event extractors often yield many noisy event role filler extractions in the absence of a broader view of the document-level context. Filtering spurious extractions and aggregating event information in a document remains a challenging problem. Following the observation that a document has several relevant event regions densely populated with event role fillers, we build graphs with candidate role filler extractions enriched by sentential embeddings as nodes, and use graph attention networks to identify event regions in a document and aggregate event information. We characterize edges between candidate extractions in a graph into rich vector representations to facilitate event region identification. The experimental results on two datasets of two languages show that our approach yields new state-of-the-art performance for the challenging event extraction task.
Recipe Instruction Semantics Corpus (RISeC): Resolving Semantic Structure and Zero Anaphora in Recipes
Authors: Yiwei Jiang, Klim Zaporojets, Johannes Deleu, Thomas Demeester, Chris Develder
Date: 2020-12-01 00:00:00
Proceeding: Asian Chapter of the Association for Computational Linguistics 2020
Tasks: Question Answering, Reading Comprehension, Relation, Relation Extraction, Text Generation

We propose a newly annotated dataset for information extraction on recipes. Unlike previous approaches to machine comprehension of procedural texts, we avoid a priori pre-defining domain-specific predicates to recognize (e.g., the primitive instructionsin MILK) and focus on basic understanding of the expressed semantics rather than directly reduce them to a simplified state representation (e.g., ProPara). We thus frame the semantic comprehension of procedural text such as recipes, as fairly generic NLP subtasks, covering (i) entity recognition (ingredients, tools and actions), (ii) relation extraction (what ingredients and tools are involved in the actions), and (iii) zero anaphora resolution (link actions to implicit arguments, e.g., results from previous recipe steps). Further, our Recipe Instruction Semantic Corpus (RISeC) dataset includes textual descriptions for the zero anaphora, to facilitate language generation thereof. Besides the dataset itself, we contribute a pipeline neural architecture that addresses entity and relation extractionas well an identification of zero anaphora. These basic building blocks can facilitate more advanced downstream applications (e.g., question answering, conversational agents).
Stronger Baselines for Grammatical Error Correction Using a Pretrained Encoder-Decoder Model
Authors: Satoru Katsumata, Mamoru Komachi
Date: 2020-12-01 00:00:00
Proceeding: Asian Chapter of the Association for Computational Linguistics 2020
Tasks: Decoder, Grammatical Error Correction

Studies on grammatical error correction (GEC) have reported on the effectiveness of pretraining a Seq2Seq model with a large amount of pseudodata. However, this approach requires time-consuming pretraining of GEC because of the size of the pseudodata. In this study, we explored the utility of bidirectional and auto-regressive transformers (BART) as a generic pretrained encoder-decoder model for GEC. With the use of this generic pretrained model for GEC, the time-consuming pretraining can be eliminated. We find that monolingual and multilingual BART models achieve high performance in GEC, with one of the results being comparable to the current strong results in English GEC.
Sina Mandarin Alphabetical Words:A Web-driven Code-mixing Lexical Resource
Authors: Rong Xiang, Mingyu Wan, Qi Su, Chu-Ren Huang, Qin Lu
Date: 2020-12-01 00:00:00
Proceeding: Asian Chapter of the Association for Computational Linguistics 2020

Mandarin Alphabetical Word (MAW) is one indispensable component of Modern Chinese that demonstrates unique code-mixing idiosyncrasies influenced by language exchanges. Yet, this interesting phenomenon has not been properly addressed and is mostly excluded from the Chinese language system. This paper addresses the core problem of MAW identification and proposes to construct a large collection of MAWs from Sina Weibo (SMAW) using an automatic web-based technique which includes rule-based identification, informatics-based extraction, as well as Baidu search engine validation. A collection of 16,207 qualified SMAWs are obtained using this technique along with an annotated corpus of more than 200,000 sentences for linguistic research and applicable inquiries.
Compressing Pre-trained Language Models by Matrix Decomposition
Authors: Matan Ben Noach, Yoav Goldberg
Date: 2020-12-01 00:00:00
Proceeding: Asian Chapter of the Association for Computational Linguistics 2020
Tasks: Model Compression

Large pre-trained language models reach state-of-the-art results on many different NLP tasks when fine-tuned individually; They also come with a significant memory and computational requirements, calling for methods to reduce model sizes (green AI). We propose a two-stage model-compression method to reduce a model{'}s inference time cost. We first decompose the matrices in the model into smaller matrices and then perform feature distillation on the internal representation to recover from the decomposition. This approach has the benefit of reducing the number of parameters while preserving much of the information within the model. We experimented on BERT-base model with the GLUE benchmark dataset and show that we can reduce the number of parameters by a factor of 0.4x, and increase inference speed by a factor of 1.45x, while maintaining a minimal loss in metric performance.
You May Like This Hotel Because ...: Identifying Evidence for Explainable Recommendations
Authors: Shin Kanouchi, Masato Neishi, Yuta Hayashibe, Hiroki Ouchi, Naoaki Okazaki
Date: 2020-12-01 00:00:00
Proceeding: Asian Chapter of the Association for Computational Linguistics 2020
Tasks: Explainable Recommendation, Sentence

Explainable recommendation is a good way to improve user satisfaction. However, explainable recommendation in dialogue is challenging since it has to handle natural language as both input and output. To tackle the challenge, this paper proposes a novel and practical task to explain evidences in recommending hotels given vague requests expressed freely in natural language. We decompose the process into two subtasks on hotel reviews: Evidence Identification and Evidence Explanation. The former predicts whether or not a sentence contains evidence that expresses why a given request is satisfied. The latter generates a recommendation sentence given a request and an evidence sentence. In order to address these subtasks, we build an Evidence-based Explanation dataset, which is the largest dataset for explaining evidences in recommending hotels for vague requests. The experimental results demonstrate that the BERT model can find evidence sentences with respect to various vague requests and that the LSTM-based model can generate recommendation sentences.
A Unified Framework for Multilingual and Code-Mixed Visual Question Answering
Authors: Deepak Gupta, Pabitra Lenka, Asif Ekbal, Pushpak Bhattacharyya
Date: 2020-12-01 00:00:00
Proceeding: Asian Chapter of the Association for Computational Linguistics 2020
Tasks: Question Answering, Visual Question Answering, Visual Question Answering (VQA)

In this paper, we propose an effective deep learning framework for multilingual and code- mixed visual question answering. The pro- posed model is capable of predicting answers from the questions in Hindi, English or Code- mixed (Hinglish: Hindi-English) languages. The majority of the existing techniques on Vi- sual Question Answering (VQA) focus on En- glish questions only. However, many applica- tions such as medical imaging, tourism, visual assistants require a multilinguality-enabled module for their widespread usages. As there is no available dataset in English-Hindi VQA, we firstly create Hindi and Code-mixed VQA datasets by exploiting the linguistic properties of these languages. We propose a robust tech- nique capable of handling the multilingual and code-mixed question to provide the answer against the visual information (image). To better encode the multilingual and code-mixed questions, we introduce a hierarchy of shared layers. We control the behaviour of these shared layers by an attention-based soft layer sharing mechanism, which learns how shared layers are applied in different ways for the dif- ferent languages of the question. Further, our model uses bi-linear attention with a residual connection to fuse the language and image fea- tures. We perform extensive evaluation and ablation studies for English, Hindi and Code- mixed VQA. The evaluation shows that the proposed multilingual model achieves state-of- the-art performance in all these settings.
AMesure: A Web Platform to Assist the Clear Writing of Administrative Texts
Authors: Thomas Fran{\c{c}}ois, Adeline M{\"u}ller, Eva Rolin, Magali Norr{\'e}
Date: 2020-12-01 00:00:00
Proceeding: Asian Chapter of the Association for Computational Linguistics 2020

This article presents the AMesure platform, which aims to assist writers of French administrative texts in simplifying their writing. This platform includes a readability formula specialized for administrative texts and it also uses various natural language processing (NLP) tools to analyze texts and highlight a number of linguistic phenomena considered difficult to read. Finally, based on the difficulties identified, it offers pieces of advice coming from official plain language guides to users. This paper describes the different components of the system and reports an evaluation of these components.
metaCAT: A Metadata-based Task-oriented Chatbot Annotation Tool
Authors: Ximing Liu, Wei Xue, Qi Su, Weiran Nie, Wei Peng
Date: 2020-12-01 00:00:00
Proceeding: Asian Chapter of the Association for Computational Linguistics 2020
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Chatbot, speech-recognition, Speech Recognition

Creating high-quality annotated dialogue corpora is challenging. It is essential to develop practical annotation tools to support humans in this time-consuming and error-prone task. We present metaCAT, which is an open-source web-based annotation tool designed specifically for developing task-oriented dialogue data. To the best of our knowledge, metaCAT is the first annotation tool that provides comprehensive metadata annotation coverage to the domain, intent, and span information. The data annotation quality is enhanced by a real-time annotation constraint-checking mechanism. An Automatic Speech Recognition (ASR) function is implemented to allow users to paraphrase and create more diversified annotated utterances. metaCAT is publicly available for the community.