pwc-archive/papers-with-abstracts huggingface dataset:
Discourse as a Function of Event: Profiling Discourse Structure in News Articles around the Main Event
Authors: Prafulla Kumar Choubey, Aaron Lee, Ruihong Huang, Lu Wang
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Argument Mining, Articles, coreference-resolution, Coreference Resolution, Event Coreference Resolution, Text Classification

Understanding discourse structures of news articles is vital to effectively contextualize the occurrence of a news event. To enable computational modeling of news structures, we apply an existing theory of functional discourse structure for news articles that revolves around the main event and create a human-annotated corpus of 802 documents spanning over four domains and three media sources. Next, we propose several document-level neural-network models to automatically construct news content structures. Finally, we demonstrate that incorporating system predicted news structures yields new state-of-the-art performance for event coreference resolution. The news documents we annotated are openly available and the annotations are publicly released for future research.
Implicit Discourse Relation Classification: We Need to Talk about Evaluation
Authors: Najoung Kim, Song Feng, Chulaka Gunasekara, Luis Lastras
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Classification, General Classification, Implicit Discourse Relation Classification, Relation, Relation Classification, Sentence

Implicit relation classification on Penn Discourse TreeBank (PDTB) 2.0 is a common benchmark task for evaluating the understanding of discourse relations. However, the lack of consistency in preprocessing and evaluation poses challenges to fair comparison of results in the literature. In this work, we highlight these inconsistencies and propose an improved evaluation protocol. Paired with this protocol, we report strong baseline results from pretrained sentence encoders, which set the new state-of-the-art for PDTB 2.0. Furthermore, this work is the first to explore fine-grained relation classification on PDTB 3.0. We expect our work to serve as a point of comparison for future work, and also as an initiative to discuss models of larger context and possible data augmentations for downstream transferability.
ZPR2: Joint Zero Pronoun Recovery and Resolution using Multi-Task Learning and BERT
Authors: Linfeng Song, Kun Xu, Yue Zhang, Jianshu Chen, Dong Yu
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Multi-Task Learning

Zero pronoun recovery and resolution aim at recovering the dropped pronoun and pointing out its anaphoric mentions, respectively. We propose to better explore their interaction by solving both tasks together, while the previous work treats them separately. For zero pronoun resolution, we study this task in a more realistic setting, where no parsing trees or only automatic trees are available, while most previous work assumes gold trees. Experiments on two benchmarks show that joint modeling significantly outperforms our baseline that already beats the previous state of the arts.
Language (Technology) is Power: A Critical Survey of ``Bias'' in NLP
Authors: Su Lin Blodgett, Solon Barocas, Hal Daum{\'e} III, Hanna Wallach
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6

We survey 146 papers analyzing {``}bias{''} in NLP systems, finding that their motivations are often vague, inconsistent, and lacking in normative reasoning, despite the fact that analyzing {``}bias{''} is an inherently normative process. We further find that these papers{'} proposed quantitative techniques for measuring or mitigating {``}bias{''} are poorly matched to their motivations and do not engage with the relevant literature outside of NLP. Based on these findings, we describe the beginnings of a path forward by proposing three recommendations that should guide work analyzing {``}bias{''} in NLP systems. These recommendations rest on a greater recognition of the relationships between language and social hierarchies, encouraging researchers and practitioners to articulate their conceptualizations of {``}bias{''}---i.e., what kinds of system behaviors are harmful, in what ways, to whom, and why, as well as the normative reasoning underlying these statements{---}and to center work around the lived experiences of members of communities affected by NLP systems, while interrogating and reimagining the power relations between technologists and such communities.
Benefits of Intermediate Annotations in Reading Comprehension
Authors: Dheeru Dua, Sameer Singh, Matt Gardner
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Reading Comprehension

Complex compositional reading comprehension datasets require performing latent sequential decisions that are learned via supervision from the final answer. A large combinatorial space of possible decision paths that result in the same answer, compounded by the lack of intermediate supervision to help choose the right path, makes the learning particularly hard for this task. In this work, we study the benefits of collecting intermediate reasoning supervision along with the answer during data collection. We find that these intermediate annotations can provide two-fold benefits. First, we observe that for any collection budget, spending a fraction of it on intermediate annotations results in improved model performance, for two complex compositional datasets: DROP and Quoref. Second, these annotations encourage the model to learn the correct latent reasoning steps, helping combat some of the biases introduced during the data collection process.
On the Importance of Diversity in Question Generation for QA
Authors: Md. Arafat Sultan, Ch, Shubham el, Fern, Ram{\'o}n ez Astudillo, Vittorio Castelli
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Diversity, Question Answering, Question Generation, Question-Generation

Automatic question generation (QG) has shown promise as a source of synthetic training data for question answering (QA). In this paper we ask: Is textual diversity in QG beneficial for downstream QA? Using top-p nucleus sampling to derive samples from a transformer-based question generator, we show that diversity-promoting QG indeed provides better QA training than likelihood maximization approaches such as beam search. We also show that standard QG evaluation metrics such as BLEU, ROUGE and METEOR are inversely correlated with diversity, and propose a diversity-aware intrinsic measure of overall QG quality that correlates well with extrinsic evaluation on QA.
Agreement Prediction of Arguments in Cyber Argumentation for Detecting Stance Polarity and Intensity
Authors: Joseph Sirrianni, Xiaoqing Liu, Douglas Adams
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: regression, Stance Detection

In online debates, users express different levels of agreement/disagreement with one another{'}s arguments and ideas. Often levels of agreement/disagreement are implicit in the text, and must be predicted to analyze collective opinions. Existing stance detection methods predict the polarity of a post{'}s stance toward a topic or post, but don{'}t consider the stance{'}s degree of intensity. We introduce a new research problem, stance polarity and intensity prediction in response relationships between posts. This problem is challenging because differences in stance intensity are often subtle and require nuanced language understanding. Cyber argumentation research has shown that incorporating both stance polarity and intensity data in online debates leads to better discussion analysis. We explore five different learning models: Ridge-M regression, Ridge-S regression, SVR-RF-R, pkudblab-PIP, and T-PAN-PIP for predicting stance polarity and intensity in argumentation. These models are evaluated using a new dataset for stance polarity and intensity prediction collected using a cyber argumentation platform. The SVR-RF-R model performs best for prediction of stance polarity with an accuracy of 70.43{\%} and intensity with RMSE of 0.596. This work is the first to train models for predicting a post{'}s stance polarity and intensity in one combined value in cyber argumentation with reasonably good accuracy.
Cross-Lingual Unsupervised Sentiment Classification with Multi-View Transfer Learning
Authors: Hongliang Fei, Ping Li
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Classification, Cross-Lingual Sentiment Classification, Decoder, General Classification, Language Modeling, Language Modelling, Machine Translation, Sentiment Analysis, Sentiment Classification, Transfer Learning, Translation, Unsupervised Machine Translation

Recent neural network models have achieved impressive performance on sentiment classification in English as well as other languages. Their success heavily depends on the availability of a large amount of labeled data or parallel corpus. In this paper, we investigate an extreme scenario of cross-lingual sentiment classification, in which the low-resource language does not have any labels or parallel corpus. We propose an unsupervised cross-lingual sentiment classification model named multi-view encoder-classifier (MVEC) that leverages an unsupervised machine translation (UMT) system and a language discriminator. Unlike previous language model (LM) based fine-tuning approaches that adjust parameters solely based on the classification error on training data, we employ the encoder-decoder framework of a UMT as a regularization component on the shared network parameters. In particular, the cross-lingual encoder of our model learns a shared representation, which is effective for both reconstructing input sentences of two languages and generating more representative views from the input for classification. Extensive experiments on five language pairs verify that our model significantly outperforms other models for 8/11 sentiment classification tasks.
Efficient Pairwise Annotation of Argument Quality
Authors: Lukas Gienapp, Benno Stein, Matthias Hagen, Martin Potthast
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6

We present an efficient annotation framework for argument quality, a feature difficult to be measured reliably as per previous work. A stochastic transitivity model is combined with an effective sampling strategy to infer high-quality labels with low effort from crowdsourced pairwise judgments. The model{'}s capabilities are showcased by compiling Webis-ArgQuality-20, an argument quality corpus that comprises scores for rhetorical, logical, dialectical, and overall quality inferred from a total of 41,859 pairwise judgments among 1,271 arguments. With up to 93{\%} cost savings, our approach significantly outperforms existing annotation procedures. Furthermore, novel insight into argument quality is provided through statistical analysis, and a new aggregation method to infer overall quality from individual quality dimensions is proposed.
Entity-Aware Dependency-Based Deep Graph Attention Network for Comparative Preference Classification
Authors: Nianzu Ma, Sahisnu Mazumder, Hao Wang, Bing Liu
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: General Classification, Graph Attention, Sentence, Word Embeddings

This paper studies the task of comparative preference classification (CPC). Given two entities in a sentence, our goal is to classify whether the first (or the second) entity is preferred over the other or no comparison is expressed at all between the two entities. Existing works either do not learn entity-aware representations well and fail to deal with sentences involving multiple entity pairs or use sequential modeling approaches that are unable to capture long-range dependencies between the entities. Some also use traditional machine learning approaches that do not generalize well. This paper proposes a novel Entity-aware Dependency-based Deep Graph Attention Network (ED-GAT) that employs a multi-hop graph attention over a dependency graph sentence representation to leverage both the semantic information from word embeddings and the syntactic information from the dependency graph to solve the problem. Empirical evaluation shows that the proposed model achieves the state-of-the-art performance in comparative preference classification.
A Comprehensive Analysis of Preprocessing for Word Representation Learning in Affective Tasks
Authors: Nastaran Babanejad, Ameeta Agrawal, Aijun An, Manos Papagelis
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Emotion Classification, Representation Learning, Sarcasm Detection, Sentiment Analysis

Affective tasks such as sentiment analysis, emotion classification, and sarcasm detection have been popular in recent years due to an abundance of user-generated data, accurate computational linguistic models, and a broad range of relevant applications in various domains. At the same time, many studies have highlighted the importance of text preprocessing, as an integral step to any natural language processing prediction model and downstream task. While preprocessing in affective systems is well-studied, preprocessing in word vector-based models applied to affective systems, is not. To address this limitation, we conduct a comprehensive analysis of the role of preprocessing techniques in affective analysis based on word vector models. Our analysis is the first of its kind and provides useful insights of the importance of each preprocessing technique when applied at the training phase, commonly ignored in pretrained word vector models, and/or at the downstream task phase.
Diverse and Informative Dialogue Generation with Context-Specific Commonsense Knowledge Awareness
Authors: Sixing Wu, Ying Li, Dawei Zhang, Yang Zhou, Zhonghai Wu
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Dialogue Generation, Knowledge Graphs

Generative dialogue systems tend to produce generic responses, which often leads to boring conversations. For alleviating this issue, Recent studies proposed to retrieve and introduce knowledge facts from knowledge graphs. While this paradigm works to a certain extent, it usually retrieves knowledge facts only based on the entity word itself, without considering the specific dialogue context. Thus, the introduction of the context-irrelevant knowledge facts can impact the quality of generations. To this end, this paper proposes a novel commonsense knowledge-aware dialogue generation model, ConKADI. We design a Felicitous Fact mechanism to help the model focus on the knowledge facts that are highly relevant to the context; furthermore, two techniques, Context-Knowledge Fusion and Flexible Mode Fusion are proposed to facilitate the integration of the knowledge in the ConKADI. We collect and build a large-scale Chinese dataset aligned with the commonsense knowledge for dialogue generation. Extensive evaluations over both an open-released English dataset and our Chinese dataset demonstrate that our approach ConKADI outperforms the state-of-the-art approach CCM, in most experiments.
Improving Event Detection via Open-domain Trigger Knowledge
Authors: Meihan Tong, Bin Xu, Shuai Wang, Yixin Cao, Lei Hou, Juanzi Li, Jun Xie
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Event Detection, Knowledge Distillation

Event Detection (ED) is a fundamental task in automatically structuring texts. Due to the small scale of training data, previous methods perform poorly on unseen/sparsely labeled trigger words and are prone to overfitting densely labeled trigger words. To address the issue, we propose a novel Enrichment Knowledge Distillation (EKD) model to leverage external open-domain trigger knowledge to reduce the in-built biases to frequent trigger words in annotations. Experiments on benchmark ACE2005 show that our model outperforms nine strong baselines, is especially effective for unseen/sparsely labeled trigger words. The source code is released on https://github.com/shuaiwa16/ekd.git.
Improving Low-Resource Named Entity Recognition using Joint Sentence and Token Labeling
Authors: Canasai Kruengkrai, Thien Hai Nguyen, Sharifah Mahani Aljunied, Lidong Bing
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Binary Classification, Classification, General Classification, Low Resource Named Entity Recognition, Multi-class Classification, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Sentence

Exploiting sentence-level labels, which are easy to obtain, is one of the plausible methods to improve low-resource named entity recognition (NER), where token-level labels are costly to annotate. Current models for jointly learning sentence and token labeling are limited to binary classification. We present a joint model that supports multi-class classification and introduce a simple variant of self-attention that allows the model to learn scaling factors. Our model produces 3.78{\%}, 4.20{\%}, 2.08{\%} improvements in F1 over the BiLSTM-CRF baseline on e-commerce product titles in three different low-resource languages: Vietnamese, Thai, and Indonesian, respectively.
Multi-Cell Compositional LSTM for NER Domain Adaptation
Authors: Chen Jia, Yue Zhang
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Domain Adaptation, Multi-Task Learning, NER, Transfer Learning, Vocal Bursts Type Prediction

Cross-domain NER is a challenging yet practical problem. Entity mentions can be highly different across domains. However, the correlations between entity types can be relatively more stable across domains. We investigate a multi-cell compositional LSTM structure for multi-task learning, modeling each entity type using a separate cell state. With the help of entity typed units, cross-domain knowledge transfer can be made in an entity type level. Theoretically, the resulting distinct feature distributions for each entity type make it more powerful for cross-domain transfer. Empirically, experiments on four few-shot and zero-shot datasets show our method significantly outperforms a series of multi-task learning methods and achieves the best results.
Pyramid: A Layered Model for Nested Named Entity Recognition
Authors: Jue Wang, Lidan Shou, Ke Chen, Gang Chen
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Nested Named Entity Recognition

This paper presents Pyramid, a novel layered model for Nested Named Entity Recognition (nested NER). In our approach, token or text region embeddings are recursively inputted into L flat NER layers, from bottom to top, stacked in a pyramid shape. Each time an embedding passes through a layer of the pyramid, its length is reduced by one. Its hidden state at layer l represents an l-gram in the input text, which is labeled only if its corresponding text region represents a complete entity mention. We also design an inverse pyramid to allow bidirectional interaction between layers. The proposed method achieves state-of-the-art F1 scores in nested NER on ACE-2004, ACE-2005, GENIA, and NNE, which are 80.27, 79.42, 77.78, and 93.70 with conventional embeddings, and 87.74, 86.34, 79.31, and 94.68 with pre-trained contextualized embeddings. In addition, our model can be used for the more general task of Overlapping Named Entity Recognition. A preliminary experiment confirms the effectiveness of our method in overlapping NER.
ReInceptionE: Relation-Aware Inception Network with Joint Local-Global Structural Information for Knowledge Graph Embedding
Authors: Zhiwen Xie, Guangyou Zhou, Jin Liu, Jimmy Xiangji Huang
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Graph Embedding, Knowledge Graph Embedding, Relation

The goal of Knowledge graph embedding (KGE) is to learn how to represent the low dimensional vectors for entities and relations based on the observed triples. The conventional shallow models are limited to their expressiveness. ConvE (Dettmers et al., 2018) takes advantage of CNN and improves the expressive power with parameter efficient operators by increasing the interactions between head and relation embeddings. However, there is no structural information in the embedding space of ConvE, and the performance is still limited by the number of interactions. The recent KBGAT (Nathani et al., 2019) provides another way to learn embeddings by adaptively utilizing structural information. In this paper, we take the benefits of ConvE and KBGAT together and propose a Relation-aware Inception network with joint local-global structural information for knowledge graph Embedding (ReInceptionE). Specifically, we first explore the Inception network to learn query embedding, which aims to further increase the interactions between head and relation embeddings. Then, we propose to use a relation-aware attention mechanism to enrich the query embedding with the local neighborhood and global entity information. Experimental results on both WN18RR and FB15k-237 datasets demonstrate that ReInceptionE achieves competitive performance compared with state-of-the-art methods.
Improving Neural Machine Translation with Soft Template Prediction
Authors: Jian Yang, Shuming Ma, Dong-dong Zhang, Zhoujun Li, Ming Zhou
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Decoder, Machine Translation, NMT, Prediction, Translation

Although neural machine translation (NMT) has achieved significant progress in recent years, most previous NMT models only depend on the source text to generate translation. Inspired by the success of template-based and syntax-based approaches in other fields, we propose to use extracted templates from tree structures as soft target templates to guide the translation procedure. In order to learn the syntactic structure of the target sentences, we adopt constituency-based parse tree to generate candidate templates. We incorporate the template information into the encoder-decoder framework to jointly utilize the templates and source text. Experiments show that our model significantly outperforms the baseline models on four benchmarks and demonstrates the effectiveness of soft target templates.
Tagged Back-translation Revisited: Why Does It Really Work?
Authors: Benjamin Marie, Raphael Rubino, Atsushi Fujita
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Machine Translation, NMT, TAG, Translation

In this paper, we show that neural machine translation (NMT) systems trained on large back-translated data overfit some of the characteristics of machine-translated texts. Such NMT systems better translate human-produced translations, i.e., translationese, but may largely worsen the translation quality of original texts. Our analysis reveals that adding a simple tag to back-translations prevents this quality degradation and improves on average the overall translation quality by helping the NMT system to distinguish back-translated data from original parallel data during training. We also show that, in contrast to high-resource configurations, NMT systems trained in low-resource settings are much less vulnerable to overfit back-translations. We conclude that the back-translations in the training data should always be tagged especially when the origin of the text to be translated is unknown.
Neural-DINF: A Neural Network based Framework for Measuring Document Influence
Authors: Jie Tan, Changlin Yang, Ying Li, Siliang Tang, Chen Huang, Yueting Zhuang
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Topic Models, Word Embeddings

Measuring the scholarly impact of a document without citations is an important and challenging problem. Existing approaches such as Document Influence Model (DIM) are based on dynamic topic models, which only consider the word frequency change. In this paper, we use both frequency changes and word semantic shifts to measure document influence by developing a neural network framework. Our model has three steps. Firstly, we train the word embeddings for different time periods. Subsequently, we propose an unsupervised method to align vectors for different time periods. Finally, we compute the influence value of documents. Our experimental results show that our model outperforms DIM.