pwc-archive/papers-with-abstracts huggingface dataset:
R\^3: Reverse, Retrieve, and Rank for Sarcasm Generation with Commonsense Knowledge
Authors: Tuhin Chakrabarty, Debanjan Ghosh, Smar Muresan, a, Nanyun Peng
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Sentence, World Knowledge

We propose an unsupervised approach for sarcasm generation based on a non-sarcastic input sentence. Our method employs a retrieve-and-edit framework to instantiate two major characteristics of sarcasm: reversal of valence and semantic incongruity with the context, which could include shared commonsense or world knowledge between the speaker and the listener. While prior works on sarcasm generation predominantly focus on context incongruity, we show that combining valence reversal and semantic incongruity based on the commonsense knowledge generates sarcasm of higher quality. Human evaluation shows that our system generates sarcasm better than humans 34{\%} of the time, and better than a reinforced hybrid baseline 90{\%} of the time.
A Joint Neural Model for Information Extraction with Global Features
Authors: Ying Lin, Heng Ji, Fei Huang, Lingfei Wu
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Decoder, Sentence

Most existing joint neural models for Information Extraction (IE) use local task-specific classifiers to predict labels for individual instances (e.g., trigger, relation) regardless of their interactions. For example, a victim of a die event is likely to be a victim of an attack event in the same sentence. In order to capture such cross-subtask and cross-instance inter-dependencies, we propose a joint neural framework, OneIE, that aims to extract the globally optimal IE result as a graph from an input sentence. OneIE performs end-to-end IE in four stages: (1) Encoding a given sentence as contextualized word representations; (2) Identifying entity mentions and event triggers as nodes; (3) Computing label scores for all nodes and their pairwise links using local classifiers; (4) Searching for the globally optimal graph with a beam decoder. At the decoding stage, we incorporate global features to capture the cross-subtask and cross-instance interactions. Experiments show that adding global features improves the performance of our model and achieves new state of-the-art on all subtasks. In addition, as OneIE does not use any language-specific feature, we prove it can be easily applied to new languages or trained in a multilingual manner.
Exploiting the Syntax-Model Consistency for Neural Relation Extraction
Authors: Amir Pouran Ben Veyseh, Franck Dernoncourt, Dejing Dou, Thien Huu Nguyen
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Multi-Task Learning, Relation, Relation Extraction

This paper studies the task of Relation Extraction (RE) that aims to identify the semantic relations between two entity mentions in text. In the deep learning models for RE, it has been beneficial to incorporate the syntactic structures from the dependency trees of the input sentences. In such models, the dependency trees are often used to directly structure the network architectures or to obtain the dependency relations between the word pairs to inject the syntactic information into the models via multi-task learning. The major problem with these approaches is the lack of generalization beyond the syntactic structures in the training data or the failure to capture the syntactic importance of the words for RE. In order to overcome these issues, we propose a novel deep learning model for RE that uses the dependency trees to extract the syntax-based importance scores for the words, serving as a tree representation to introduce syntactic information into the models with greater generalization. In particular, we leverage Ordered-Neuron Long-Short Term Memory Networks (ON-LSTM) to infer the model-based importance scores for RE for every word in the sentences that are then regulated to be consistent with the syntax-based scores to enable syntactic information injection. We perform extensive experiments to demonstrate the effectiveness of the proposed method, leading to the state-of-the-art performance on three RE benchmark datasets.
Learning Interpretable Relationships between Entities, Relations and Concepts via Bayesian Structure Learning on Open Domain Facts
Authors: Jingyuan Zhang, Mingming Sun, Yue Feng, Ping Li
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6

Concept graphs are created as universal taxonomies for text understanding in the open-domain knowledge. The nodes in concept graphs include both entities and concepts. The edges are from entities to concepts, showing that an entity is an instance of a concept. In this paper, we propose the task of learning interpretable relationships from open-domain facts to enrich and refine concept graphs. The Bayesian network structures are learned from open-domain facts as the interpretable relationships between relations of facts and concepts of entities. We conduct extensive experiments on public English and Chinese datasets. Compared to the state-of-the-art methods, the learned network structures help improving the identification of concepts for entities based on the relations of entities on both datasets.
A Prioritization Model for Suicidality Risk Assessment
Authors: Han-Chin Shing, Philip Resnik, Douglas Oard
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: model, Retrieval

We reframe suicide risk assessment from social media as a ranking problem whose goal is maximizing detection of severely at-risk individuals given the time available. Building on measures developed for resource-bounded document retrieval, we introduce a well founded evaluation paradigm, and demonstrate using an expert-annotated test collection that meaningful improvements over plausible cascade model baselines can be achieved using an approach that jointly ranks individuals and their social media posts.
CluHTM - Semantic Hierarchical Topic Modeling based on CluWords
Authors: Felipe Viegas, Washington Cunha, Christian Gomes, Ant{\^o}nio Pereira, Leonardo Rocha, Marcos Goncalves
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6

Hierarchical Topic modeling (HTM) exploits latent topics and relationships among them as a powerful tool for data analysis and exploration. Despite advantages over traditional topic modeling, HTM poses its own challenges, such as (1) topic incoherence, (2) unreasonable (hierarchical) structure, and (3) issues related to the definition of the {``}ideal{''} number of topics and depth of the hierarchy. In this paper, we advance the state-of-the-art on HTM by means of the design and evaluation of CluHTM, a novel non-probabilistic hierarchical matrix factorization aimed at solving the specific issues of HTM. CluHTM{'}s novel contributions include: (i) the exploration of richer text representation that encapsulates both, global (dataset level) and local semantic information {--} when combined, these pieces of information help to solve the topic incoherence problem as well as issues related to the unreasonable structure; (ii) the exploitation of a stability analysis metric for defining the number of topics and the {``}shape{''} the hierarchical structure. In our evaluation, considering twelve datasets and seven state-of-the-art baselines, CluHTM outperformed the baselines in the vast majority of the cases, with gains of around 500{\%} over the strongest state-of-the-art baselines. We also provide qualitative and quantitative statistical analyses of why our solution works so well.
Feature Projection for Improved Text Classification
Authors: Qi Qin, Wenpeng Hu, Bing Liu
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Classification, General Classification, Representation Learning, Sentiment Analysis, Sentiment Classification, text-classification, Text Classification

In classification, there are usually some good features that are indicative of class labels. For example, in sentiment classification, words like good and nice are indicative of the positive sentiment and words like bad and terrible are indicative of the negative sentiment. However, there are also many common features (e.g., words) that are not indicative of any specific class (e.g., voice and screen, which are common to both sentiment classes and are not discriminative for classification). Although deep learning has made significant progresses in generating discriminative features through its powerful representation learning, we believe there is still room for improvement. In this paper, we propose a novel angle to further improve this representation learning, i.e., feature projection. This method projects existing features into the orthogonal space of the common features. The resulting projection is thus perpendicular to the common features and more discriminative for classification. We apply this new method to improve CNN, RNN, Transformer, and Bert based text classification and obtain markedly better results.
Frugal Paradigm Completion
Authors: Alex Erdmann, er, Tom Kenter, Markus Becker, Christian Schallhart
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6

Lexica distinguishing all morphologically related forms of each lexeme are crucial to many language technologies, yet building them is expensive. We propose a frugal paradigm completion approach that predicts all related forms in a morphological paradigm from as few manually provided forms as possible. It induces typological information during training which it uses to determine the best sources at test time. We evaluate our language-agnostic approach on 7 diverse languages. Compared to popular alternative approaches, ours reduces manual labor by 16-63{\%} and is the most robust to typological variation.
Improving Chinese Word Segmentation with Wordhood Memory Networks
Authors: Yuanhe Tian, Yan Song, Fei Xia, Tong Zhang, Yonggang Wang
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Chinese Word Segmentation, Decoder

Contextual features always play an important role in Chinese word segmentation (CWS). Wordhood information, being one of the contextual features, is proved to be useful in many conventional character-based segmenters. However, this feature receives less attention in recent neural models and it is also challenging to design a framework that can properly integrate wordhood information from different wordhood measures to existing neural frameworks. In this paper, we therefore propose a neural framework, WMSeg, which uses memory networks to incorporate wordhood information with several popular encoder-decoder combinations for CWS. Experimental results on five benchmark datasets indicate the memory mechanism successfully models wordhood information for neural segmenters and helps WMSeg achieve state-of-the-art performance on all those datasets. Further experiments and analyses also demonstrate the robustness of our proposed framework with respect to different wordhood measures and the efficiency of wordhood information in cross-domain experiments.
Joint Chinese Word Segmentation and Part-of-speech Tagging via Two-way Attentions of Auto-analyzed Knowledge
Authors: Yuanhe Tian, Yan Song, Xiang Ao, Fei Xia, Xiaojun Quan, Tong Zhang, Yonggang Wang
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Chinese Word Segmentation, Part-Of-Speech Tagging, POS, POS Tagging

Chinese word segmentation (CWS) and part-of-speech (POS) tagging are important fundamental tasks for Chinese language processing, where joint learning of them is an effective one-step solution for both tasks. Previous studies for joint CWS and POS tagging mainly follow the character-based tagging paradigm with introducing contextual information such as n-gram features or sentential representations from recurrent neural models. However, for many cases, the joint tagging needs not only modeling from context features but also knowledge attached to them (e.g., syntactic relations among words); limited efforts have been made by existing research to meet such needs. In this paper, we propose a neural model named TwASP for joint CWS and POS tagging following the character-based sequence labeling paradigm, where a two-way attention mechanism is used to incorporate both context feature and their corresponding syntactic knowledge for each input character. Particularly, we use existing language processing toolkits to obtain the auto-analyzed syntactic knowledge for the context, and the proposed attention module can learn and benefit from them although their quality may not be perfect. Our experiments illustrate the effectiveness of the two-way attentions for joint CWS and POS tagging, where state-of-the-art performance is achieved on five benchmark datasets.
Beyond Possession Existence: Duration and Co-Possession
Authors: Dhivya Chinnappa, Srikala Murugan, Eduardo Blanco
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6

This paper introduces two tasks: determining (a) the duration of possession relations and (b) co-possessions, i.e., whether multiple possessors possess a possessee at the same time. We present new annotations on top of corpora annotating possession existence and experimental results. Regarding possession duration, we derive the time spans we work with empirically from annotations indicating lower and upper bounds. Regarding co-possessions, we use a binary label. Cohen{'}s kappa coefficients indicate substantial agreement, and experimental results show that text is more useful than the image for solving these tasks.
Estimating Mutual Information Between Dense Word Embeddings
Authors: Vitalii Zhelezniak, Aleks Savkov, ar, Nils Hammerla
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Semantic Textual Similarity, STS, Word Embeddings

Word embedding-based similarity measures are currently among the top-performing methods on unsupervised semantic textual similarity (STS) tasks. Recent work has increasingly adopted a statistical view on these embeddings, with some of the top approaches being essentially various correlations (which include the famous cosine similarity). Another excellent candidate for a similarity measure is mutual information (MI), which can capture arbitrary dependencies between the variables and has a simple and intuitive expression. Unfortunately, its use in the context of dense word embeddings has so far been avoided due to difficulties with estimating MI for continuous data. In this work we go through a vast literature on estimating MI in such cases and single out the most promising methods, yielding a simple and elegant similarity measure for word embeddings. We show that mutual information is a viable alternative to correlations, gives an excellent signal that correlates well with human judgements of similarity and rivals existing state-of-the-art unsupervised methods.
Exploring Unexplored Generalization Challenges for Cross-Database Semantic Parsing
Authors: Alane Suhr, Ming-Wei Chang, Peter Shaw, Kenton Lee
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Semantic Parsing

We study the task of cross-database semantic parsing (XSP), where a system that maps natural language utterances to executable SQL queries is evaluated on databases unseen during training. Recently, several datasets, including Spider, were proposed to support development of XSP systems. We propose a challenging evaluation setup for cross-database semantic parsing, focusing on variation across database schemas and in-domain language use. We re-purpose eight semantic parsing datasets that have been well-studied in the setting where in-domain training data is available, and instead use them as additional evaluation data for XSP systems instead. We build a system that performs well on Spider, and find that it struggles to generalize to our re-purposed set. Our setup uncovers several generalization challenges for cross-database semantic parsing, demonstrating the need to use and develop diverse training and evaluation datasets.
Predicting the Focus of Negation: Model and Error Analysis
Authors: Md Mosharaf Hossain, Kathleen Hamilton, Alexis Palmer, Eduardo Blanco
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Negation

The focus of a negation is the set of tokens intended to be negated, and a key component for revealing affirmative alternatives to negated utterances. In this paper, we experiment with neural networks to predict the focus of negation. Our main novelty is leveraging a scope detector to introduce the scope of negation as an additional input to the network. Experimental results show that doing so obtains the best results to date. Additionally, we perform a detailed error analysis providing insights into the main error categories, and analyze errors depending on whether the model takes into account scope and context information.
A Generate-and-Rank Framework with Semantic Type Regularization for Biomedical Concept Normalization
Authors: Dongfang Xu, Zeyu Zhang, Steven Bethard
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6

Concept normalization, the task of linking textual mentions of concepts to concepts in an ontology, is challenging because ontologies are large. In most cases, annotated datasets cover only a small sample of the concepts, yet concept normalizers are expected to predict all concepts in the ontology. In this paper, we propose an architecture consisting of a candidate generator and a list-wise ranker based on BERT. The ranker considers pairings of concept mentions and candidate concepts, allowing it to make predictions for any concept, not just those seen during training. We further enhance this list-wise approach with a semantic type regularizer that allows the model to incorporate semantic type information from the ontology during training. Our proposed concept normalization framework achieves state-of-the-art performance on multiple datasets.
Multi-Domain Named Entity Recognition with Genre-Aware and Agnostic Inference
Authors: Jing Wang, Mayank Kulkarni, Daniel Preotiuc-Pietro
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Multi-Task Learning, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

Named entity recognition is a key component of many text processing pipelines and it is thus essential for this component to be robust to different types of input. However, domain transfer of NER models with data from multiple genres has not been widely studied. To this end, we conduct NER experiments in three predictive setups on data from: a) multiple domains; b) multiple domains where the genre label is unknown at inference time; c) domains not encountered in training. We introduce a new architecture tailored to this task by using shared and private domain parameters and multi-task learning. This consistently outperforms all other baseline and competitive methods on all three experimental setups, with differences ranging between +1.95 to +3.11 average F1 across multiple genres when compared to standard approaches. These results illustrate the challenges that need to be taken into account when building real-world NLP applications that are robust to various types of text and the methods that can help, at least partially, alleviate these issues.
Addressing Posterior Collapse with Mutual Information for Improved Variational Neural Machine Translation
Authors: Arya D. McCarthy, Xi-An Li, Jiatao Gu, Ning Dong
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: de-en, Machine Translation, NMT, Translation

This paper proposes a simple and effective approach to address the problem of posterior collapse in conditional variational autoencoders (CVAEs). It thus improves performance of machine translation models that use noisy or monolingual data, as well as in conventional settings. Extending Transformer and conditional VAEs, our proposed latent variable model measurably prevents posterior collapse by (1) using a modified evidence lower bound (ELBO) objective which promotes mutual information between the latent variable and the target, and (2) guiding the latent variable with an auxiliary bag-of-words prediction task. As a result, the proposed model yields improved translation quality compared to existing variational NMT models on WMT Ro↔En and De↔En. With latent variables being effectively utilized, our model demonstrates improved robustness over non-latent Transformer in handling uncertainty: exploiting noisy source-side monolingual data (up to +3.2 BLEU), and training with weakly aligned web-mined parallel data (up to +4.7 BLEU).
Clinical Concept Linking with Contextualized Neural Representations
Authors: Elliot Schumacher, Andriy Mulyar, Mark Dredze
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Entity Linking

In traditional approaches to entity linking, linking decisions are based on three sources of information {--} the similarity of the mention string to an entity{'}s name, the similarity of the context of the document to the entity, and broader information about the knowledge base (KB). In some domains, there is little contextual information present in the KB and thus we rely more heavily on mention string similarity. We consider one example of this, concept linking, which seeks to link mentions of medical concepts to a medical concept ontology. We propose an approach to concept linking that leverages recent work in contextualized neural models, such as ELMo (Peters et al. 2018), which create a token representation that integrates the surrounding context of the mention and concept name. We find a neural ranking approach paired with contextualized embeddings provides gains over a competitive baseline (Leaman et al. 2013). Additionally, we find that a pre-training step using synonyms from the ontology offers a useful initialization for the ranker.
Multi-Label and Multilingual News Framing Analysis
Authors: Afra Feyza Aky{\"u}rek, Lei Guo, R Elanwar, a, Prakash Ishwar, Margrit Betke, Derry Tanti Wijaya
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Transfer Learning, Translation

News framing refers to the practice in which aspects of specific issues are highlighted in the news to promote a particular interpretation. In NLP, although recent works have studied framing in English news, few have studied how the analysis can be extended to other languages and in a multi-label setting. In this work, we explore multilingual transfer learning to detect multiple frames from just the news headline in a genuinely low-resource context where there are few/no frame annotations in the target language. We propose a novel method that can leverage elementary resources consisting of a dictionary and few annotations to detect frames in the target language. Our method performs comparably or better than translating the entire target language headline to the source language for which we have annotated data. This work opens up an exciting new capability of scaling up frame analysis to many languages, even those without existing translation technologies. Lastly, we apply our method to detect frames on the issue of U.S. gun violence in multiple languages and obtain exciting insights on the relationship between different frames of the same problem across different countries with different languages.
Smart To-Do: Automatic Generation of To-Do Items from Emails
Authors: Sudipto Mukherjee, Subhabrata Mukherjee, Marcello Hasegawa, Ahmed Hassan Awadallah, Ryen White
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Management, Text Generation

Intelligent features in email service applications aim to increase productivity by helping people organize their folders, compose their emails and respond to pending tasks. In this work, we explore a new application, Smart-To-Do, that helps users with task management over emails. We introduce a new task and dataset for automatically generating To-Do items from emails where the sender has promised to perform an action. We design a two-stage process leveraging recent advances in neural text generation and sequence-to-sequence learning, obtaining BLEU and ROUGE scores of 0.23 and 0.63 for this task. To the best of our knowledge, this is the first work to address the problem of composing To-Do items from emails.