pwc-archive/papers-with-abstracts huggingface dataset:
Unsupervised FAQ Retrieval with Question Generation and BERT
Authors: Yosi Mass, Boaz Carmeli, Haggai Roitman, David Konopnicki
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Question Generation, Question-Generation, Retrieval

We focus on the task of Frequently Asked Questions (FAQ) retrieval. A given user query can be matched against the questions and/or the answers in the FAQ. We present a fully unsupervised method that exploits the FAQ pairs to train two BERT models. The two models match user queries to FAQ answers and questions, respectively. We alleviate the missing labeled data of the latter by automatically generating high-quality question paraphrases. We show that our model is on par and even outperforms supervised models on existing datasets.
``The Boating Store Had Its Best Sail Ever'': Pronunciation-attentive Contextualized Pun Recognition
Authors: Yichao Zhou, Jyun-Yu Jiang, Jieyu Zhao, Kai-Wei Chang, Wei Wang
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Sentence

Humor plays an important role in human languages and it is essential to model humor when building intelligence systems. Among different forms of humor, puns perform wordplay for humorous effects by employing words with double entendre and high phonetic similarity. However, identifying and modeling puns are challenging as puns usually involved implicit semantic or phonological tricks. In this paper, we propose Pronunciation-attentive Contextualized Pun Recognition (PCPR) to perceive human humor, detect if a sentence contains puns and locate them in the sentence. PCPR derives contextualized representation for each word in a sentence by capturing the association between the surrounding context and its corresponding phonetic symbols. Extensive experiments are conducted on two benchmark datasets. Results demonstrate that the proposed approach significantly outperforms the state-of-the-art methods in pun detection and location tasks. In-depth analyses verify the effectiveness and robustness of PCPR.
Fine-grained Interest Matching for Neural News Recommendation
Authors: Heyuan Wang, Fangzhao Wu, Zheng Liu, Xing Xie
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: News Recommendation

Personalized news recommendation is a critical technology to improve users{'} online news reading experience. The core of news recommendation is accurate matching between user{'}s interests and candidate news. The same user usually has diverse interests that are reflected in different news she has browsed. Meanwhile, important semantic features of news are implied in text segments of different granularities. Existing studies generally represent each user as a single vector and then match the candidate news vector, which may lose fine-grained information for recommendation. In this paper, we propose FIM, a Fine-grained Interest Matching method for neural news recommendation. Instead of aggregating user{'}s all historical browsed news into a unified vector, we hierarchically construct multi-level representations for each news via stacked dilated convolutions. Then we perform fine-grained matching between segment pairs of each browsed news and the candidate news at each semantic level. High-order salient signals are then identified by resembling the hierarchy of image recognition for final click prediction. Extensive experiments on a real-world dataset from MSN news validate the effectiveness of our model on news recommendation.
Interpretable Operational Risk Classification with Semi-Supervised Variational Autoencoder
Authors: Fan Zhou, Shengming Zhang, Yi Yang
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Classification, General Classification, Management, Semi-Supervised Text Classification, text-classification, Text Classification, Variational Inference

Operational risk management is one of the biggest challenges nowadays faced by financial institutions. There are several major challenges of building a text classification system for automatic operational risk prediction, including imbalanced labeled/unlabeled data and lacking interpretability. To tackle these challenges, we present a semi-supervised text classification framework that integrates multi-head attention mechanism with Semi-supervised variational inference for Operational Risk Classification (SemiORC). We empirically evaluate the framework on a real-world dataset. The results demonstrate that our method can better utilize unlabeled data and learn visually interpretable document representations. SemiORC also outperforms other baseline methods on operational risk classification.
Interpreting Twitter User Geolocation
Authors: Ting Zhong, Tianliang Wang, Fan Zhou, Goce Trajcevski, Kunpeng Zhang, Yi Yang
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6

Identifying user geolocation in online social networks is an essential task in many location-based applications. Existing methods rely on the similarity of text and network structure, however, they suffer from a lack of interpretability on the corresponding results, which is crucial for understanding model behavior. In this work, we adopt influence functions to interpret the behavior of GNN-based models by identifying the importance of training users when predicting the locations of the testing users. This methodology helps with providing meaningful explanations on prediction results. Furthermore, it also initiates an attempt to uncover the so-called {``}black-box{''} GNN-based models by investigating the effect of individual nodes.
Modeling Code-Switch Languages Using Bilingual Parallel Corpus
Authors: Gr Lee, ee, Haizhou Li
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Bilingual Lexicon Induction, Language Modeling, Language Modelling, Translation

Language modeling is the technique to estimate the probability of a sequence of words. A bilingual language model is expected to model the sequential dependency for words across languages, which is difficult due to the inherent lack of suitable training data as well as diverse syntactic structure across languages. We propose a bilingual attention language model (BALM) that simultaneously performs language modeling objective with a quasi-translation objective to model both the monolingual as well as the cross-lingual sequential dependency. The attention mechanism learns the bilingual context from a parallel corpus. BALM achieves state-of-the-art performance on the SEAME code-switch database by reducing the perplexity of 20.5{\%} over the best-reported result. We also apply BALM in bilingual lexicon induction, and language normalization tasks to validate the idea.
A Frame-based Sentence Representation for Machine Reading Comprehension
Authors: Shaoru Guo, Ru Li, Hongye Tan, Xiao-Li Li, Yong Guan, Hongyan Zhao, Yueping Zhang
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Machine Reading Comprehension, Reading Comprehension, Sentence

Sentence representation (SR) is the most crucial and challenging task in Machine Reading Comprehension (MRC). MRC systems typically only utilize the information contained in the sentence itself, while human beings can leverage their semantic knowledge. To bridge the gap, we proposed a novel Frame-based Sentence Representation (FSR) method, which employs frame semantic knowledge to facilitate sentence modelling. Specifically, different from existing methods that only model lexical units (LUs), Frame Representation Models, which utilize both LUs in frame and Frame-to-Frame (F-to-F) relations, are designed to model frames and sentences with attention schema. Our proposed FSR method is able to integrate multiple-frame semantic information to get much better sentence representations. Our extensive experimental results show that it performs better than state-of-the-art technologies on machine reading comprehension task.
Dynamic Sampling Strategies for Multi-Task Reading Comprehension
Authors: Ananth Gottumukkala, Dheeru Dua, Sameer Singh, Matt Gardner
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Multi-Task Learning, Reading Comprehension

Building general reading comprehension systems, capable of solving multiple datasets at the same time, is a recent aspirational goal in the research community. Prior work has focused on model architecture or generalization to held out datasets, and largely passed over the particulars of the multi-task learning set up. We show that a simple dynamic sampling strategy, selecting instances for training proportional to the multi-task model{'}s current performance on a dataset relative to its single task performance, gives substantive gains over prior multi-task sampling strategies, mitigating the catastrophic forgetting that is common in multi-task learning. We also demonstrate that allowing instances of different tasks to be interleaved as much as possible between each epoch and batch has a clear benefit in multitask performance over forcing task homogeneity at the epoch or batch level. Our final model shows greatly increased performance over the best model on ORB, a recently-released multitask reading comprehension benchmark.
Explicit Memory Tracker with Coarse-to-Fine Reasoning for Conversational Machine Reading
Authors: Yifan Gao, Chien-Sheng Wu, Shafiq Joty, Caiming Xiong, Richard Socher, Irwin King, Michael Lyu, Steven C. H. Hoi
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Decision Making, Reading Comprehension, Sentence

The goal of conversational machine reading is to answer user questions given a knowledge base text which may require asking clarification questions. Existing approaches are limited in their decision making due to struggles in extracting question-related rules and reasoning about them. In this paper, we present a new framework of conversational machine reading that comprises a novel Explicit Memory Tracker (EMT) to track whether conditions listed in the rule text have already been satisfied to make a decision. Moreover, our framework generates clarification questions by adopting a coarse-to-fine reasoning strategy, utilizing sentence-level entailment scores to weight token-level distributions. On the ShARC benchmark (blind, held-out) testset, EMT achieves new state-of-the-art results of 74.6{\%} micro-averaged decision accuracy and 49.5 BLEU4. We also show that EMT is more interpretable by visualizing the entailment-oriented reasoning process as the conversation flows. Code and models are released at https://github.com/Yifan-Gao/explicit{\_}memory{\_}tracker.
Learning to Identify Follow-Up Questions in Conversational Question Answering
Authors: Souvik Kundu, Qian Lin, Hwee Tou Ng
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Conversational Question Answering, Question Answering

Despite recent progress in conversational question answering, most prior work does not focus on follow-up questions. Practical conversational question answering systems often receive follow-up questions in an ongoing conversation, and it is crucial for a system to be able to determine whether a question is a follow-up question of the current conversation, for more effective answer finding subsequently. In this paper, we introduce a new follow-up question identification task. We propose a three-way attentive pooling network that determines the suitability of a follow-up question by capturing pair-wise interactions between the associated passage, the conversation history, and a candidate follow-up question. It enables the model to capture topic continuity and topic shift while scoring a particular candidate follow-up question. Experiments show that our proposed three-way attentive pooling network outperforms all baseline systems by significant margins.
Query Graph Generation for Answering Multi-hop Complex Questions from Knowledge Bases
Authors: Yunshi Lan, Jing Jiang
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Graph Generation

Previous work on answering complex questions from knowledge bases usually separately addresses two types of complexity: questions with constraints and questions with multiple hops of relations. In this paper, we handle both types of complexity at the same time. Motivated by the observation that early incorporation of constraints into query graphs can more effectively prune the search space, we propose a modified staged query graph generation method with more flexible ways to generate query graphs. Our experiments clearly show that our method achieves the state of the art on three benchmark KBQA datasets.
Improving Image Captioning Evaluation by Considering Inter References Variance
Authors: Yanzhi Yi, Hangyu Deng, Jinglu Hu
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Human Judgment Correlation, Image Captioning

Evaluating image captions is very challenging partially due to the fact that there are multiple correct captions for every single image. Most of the existing one-to-one metrics operate by penalizing mismatches between reference and generative caption without considering the intrinsic variance between ground truth captions. It usually leads to over-penalization and thus a bad correlation to human judgment. Recently, the latest one-to-one metric BERTScore can achieve high human correlation in system-level tasks while some issues can be fixed for better performance. In this paper, we propose a novel metric based on BERTScore that could handle such a challenge and extend BERTScore with a few new features appropriately for image captioning evaluation. The experimental results show that our metric achieves state-of-the-art human judgment correlation.
Moving Down the Long Tail of Word Sense Disambiguation with Gloss Informed Bi-encoders
Authors: Terra Blevins, Luke Zettlemoyer
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Word Sense Disambiguation

A major obstacle in Word Sense Disambiguation (WSD) is that word senses are not uniformly distributed, causing existing models to generally perform poorly on senses that are either rare or unseen during training. We propose a bi-encoder model that independently embeds (1) the target word with its surrounding context and (2) the dictionary definition, or gloss, of each sense. The encoders are jointly optimized in the same representation space, so that sense disambiguation can be performed by finding the nearest sense embedding for each target word embedding. Our system outperforms previous state-of-the-art models on English all-words WSD; these gains predominantly come from improved performance on rare senses, leading to a 31.1{\%} error reduction on less frequent senses over prior work. This demonstrates that rare senses can be more effectively disambiguated by modeling their definitions.
Unknown Intent Detection Using Gaussian Mixture Model with an Application to Zero-shot Intent Classification
Authors: Guangfeng Yan, Lu Fan, Qimai Li, Han Liu, Xiaotong Zhang, Xiao-Ming Wu, Albert Y. S. Lam
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Classification, General Classification, intent-classification, Intent Classification, Intent Detection, Outlier Detection, Zero-Shot Intent Classification

User intent classification plays a vital role in dialogue systems. Since user intent may frequently change over time in many realistic scenarios, unknown (new) intent detection has become an essential problem, where the study has just begun. This paper proposes a semantic-enhanced Gaussian mixture model (SEG) for unknown intent detection. In particular, we model utterance embeddings with a Gaussian mixture distribution and inject dynamic class semantic information into Gaussian means, which enables learning more class-concentrated embeddings that help to facilitate downstream outlier detection. Coupled with a density-based outlier detection algorithm, SEG achieves competitive results on three real task-oriented dialogue datasets in two languages for unknown intent detection. On top of that, we propose to integrate SEG as an unknown intent identifier into existing generalized zero-shot intent classification models to improve their performance. A case study on a state-of-the-art method, ReCapsNet, shows that SEG can push the classification performance to a significantly higher level.
Hierarchy-Aware Global Model for Hierarchical Text Classification
Authors: Jie Zhou, Chunping Ma, Dingkun Long, Guangwei Xu, Ning Ding, Haoyu Zhang, Pengjun Xie, Gongshen Liu
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Classification, General Classification, Hierarchical Multi-label Classification, model, Multi Label Text Classification, Multi-Label Text Classification, text-classification, Text Classification

Hierarchical text classification is an essential yet challenging subtask of multi-label text classification with a taxonomic hierarchy. Existing methods have difficulties in modeling the hierarchical label structure in a global view. Furthermore, they cannot make full use of the mutual interactions between the text feature space and the label space. In this paper, we formulate the hierarchy as a directed graph and introduce hierarchy-aware structure encoders for modeling label dependencies. Based on the hierarchy encoder, we propose a novel end-to-end hierarchy-aware global model (HiAGM) with two variants. A multi-label attention variant (HiAGM-LA) learns hierarchy-aware label embeddings through the hierarchy encoder and conducts inductive fusion of label-aware text features. A text feature propagation model (HiAGM-TP) is proposed as the deductive variant that directly feeds text features into hierarchy encoders. Compared with previous works, both HiAGM-LA and HiAGM-TP achieve significant and consistent improvements on three benchmark datasets.
A Graph Auto-encoder Model of Derivational Morphology
Authors: Valentin Hofmann, Hinrich Sch{\"u}tze, Janet Pierrehumbert
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: model

There has been little work on modeling the morphological well-formedness (MWF) of derivatives, a problem judged to be complex and difficult in linguistics. We present a graph auto-encoder that learns embeddings capturing information about the compatibility of affixes and stems in derivation. The auto-encoder models MWF in English surprisingly well by combining syntactic and semantic information with associative information from the mental lexicon.
Building a User-Generated Content North-African Arabizi Treebank: Tackling Hell
Authors: Djam{\'e} Seddah, Farah Essaidi, Amal Fethi, Matthieu Futeral, Benjamin Muller, Pedro Javier Ortiz Su{\'a}rez, Beno{\^\i}t Sagot, Abhishek Srivastava
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Dependency Parsing, POS, POS Tagging, Sentence, Translation

We introduce the first treebank for a romanized user-generated content variety of Algerian, a North-African Arabic dialect known for its frequent usage of code-switching. Made of 1500 sentences, fully annotated in morpho-syntax and Universal Dependency syntax, with full translation at both the word and the sentence levels, this treebank is made freely available. It is supplemented with 50k unlabeled sentences collected from Common Crawl and web-crawled data using intensive data-mining techniques. Preliminary experiments demonstrate its usefulness for POS tagging and dependency parsing. We believe that what we present in this paper is useful beyond the low-resource language community. This is the first time that enough unlabeled and annotated data is provided for an emerging user-generated content dialectal language with rich morphology and code switching, making it an challenging test-bed for most recent NLP approaches.
Crawling and Preprocessing Mailing Lists At Scale for Dialog Analysis
Authors: Janek Bevendorff, Khalid Al Khatib, Martin Potthast, Benno Stein
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6

This paper introduces the Webis Gmane Email Corpus 2019, the largest publicly available and fully preprocessed email corpus to date. We crawled more than 153 million emails from 14,699 mailing lists and segmented them into semantically consistent components using a new neural segmentation model. With 96{\%} accuracy on 15 classes of email segments, our model achieves state-of-the-art performance while being more efficient to train than previous ones. All data, code, and trained models are made freely available alongside the paper.
Multi-Hypothesis Machine Translation Evaluation
Authors: Marina Fomicheva, Lucia Specia, Francisco Guzm{\'a}n
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Machine Translation, Translation, valid

Reliably evaluating Machine Translation (MT) through automated metrics is a long-standing problem. One of the main challenges is the fact that multiple outputs can be equally valid. Attempts to minimise this issue include metrics that relax the matching of MT output and reference strings, and the use of multiple references. The latter has been shown to significantly improve the performance of evaluation metrics. However, collecting multiple references is expensive and in practice a single reference is generally used. In this paper, we propose an alternative approach: instead of modelling linguistic variation in human reference we exploit the MT model uncertainty to generate multiple diverse translations and use these: (i) as surrogates to reference translations; (ii) to obtain a quantification of translation variability to either complement existing metric scores or (iii) replace references altogether. We show that for a number of popular evaluation metrics our variability estimates lead to substantial improvements in correlation with human judgements of quality by up 15{\%}.
Multimodal Quality Estimation for Machine Translation
Authors: Shu Okabe, Fr{\'e}d{\'e}ric Blain, Lucia Specia
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Machine Translation, Sentence, Translation

We propose approaches to Quality Estimation (QE) for Machine Translation that explore both text and visual modalities for Multimodal QE. We compare various multimodality integration and fusion strategies. For both sentence-level and document-level predictions, we show that state-of-the-art neural and feature-based QE frameworks obtain better results when using the additional modality.
Attend, Translate and Summarize: An Efficient Method for Neural Cross-Lingual Summarization
Authors: Junnan Zhu, Yu Zhou, Jiajun Zhang, Cheng-qing Zong
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Decoder, Translation

Cross-lingual summarization aims at summarizing a document in one language (e.g., Chinese) into another language (e.g., English). In this paper, we propose a novel method inspired by the translation pattern in the process of obtaining a cross-lingual summary. We first attend to some words in the source text, then translate them into the target language, and summarize to get the final summary. Specifically, we first employ the encoder-decoder attention distribution to attend to the source words. Second, we present three strategies to acquire the translation probability, which helps obtain the translation candidates for each source word. Finally, each summary word is generated either from the neural distribution or from the translation candidates of source words. Experimental results on Chinese-to-English and English-to-Chinese summarization tasks have shown that our proposed method can significantly outperform the baselines, achieving comparable performance with the state-of-the-art.