pwc-archive/papers-with-abstracts huggingface dataset:
Hate-Speech and Offensive Language Detection in Roman Urdu
Authors: Hammad Rizwan, Muhammad Haroon Shakeel, Asim Karim
Date: 2020-11-01 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Transfer Learning

The task of automatic hate-speech and offensive language detection in social media content is of utmost importance due to its implications in unprejudiced society concerning race, gender, or religion. Existing research in this area, however, is mainly focused on the English language, limiting the applicability to particular demographics. Despite its prevalence, Roman Urdu (RU) lacks language resources, annotated datasets, and language models for this task. In this study, we: (1) Present a lexicon of hateful words in RU, (2) Develop an annotated dataset called RUHSOLD consisting of 10,012 tweets in RU with both coarse-grained and fine-grained labels of hate-speech and offensive language, (3) Explore the feasibility of transfer learning of five existing embedding models to RU, (4) Propose a novel deep learning architecture called CNN-gram for hate-speech and offensive language detection and compare its performance with seven current baseline approaches on RUHSOLD dataset, and (5) Train domain-specific embeddings on more than 4.7 million tweets and make them publicly available. We conclude that transfer learning is more beneficial as compared to training embedding from scratch and that the proposed model exhibits greater robustness as compared to the baselines.
Suicidal Risk Detection for Military Personnel
Authors: Sungjoon Park, Kiwoong Park, Jaimeen Ahn, Alice Oh
Date: 2020-11-01 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Ethics

We analyze social media for detecting the suicidal risk of military personnel, which is especially crucial for countries with compulsory military service such as the Republic of Korea. From a widely-used Korean social Q{\&}A site, we collect posts containing military-relevant content written by active-duty military personnel. We then annotate the posts with two groups of experts: military experts and mental health experts. Our dataset includes 2,791 posts with 13,955 corresponding expert annotations of suicidal risk levels, and this dataset is available to researchers who consent to research ethics agreement. Using various fine-tuned state-of-the-art language models, we predict the level of suicide risk, reaching .88 F1 score for classifying the risks.
Comparative Evaluation of Label-Agnostic Selection Bias in Multilingual Hate Speech Datasets
Authors: Nedjma Ousidhoum, Yangqiu Song, Dit-yan Yeung
Date: 2020-11-01 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Hate Speech Detection, Selection bias, Semantic Similarity, Semantic Textual Similarity, Topic Models

Work on bias in hate speech typically aims to improve classification performance while relatively overlooking the quality of the data. We examine selection bias in hate speech in a language and label independent fashion. We first use topic models to discover latent semantics in eleven hate speech corpora, then, we present two bias evaluation metrics based on the semantic similarity between topics and search words frequently used to build corpora. We discuss the possibility of revising the data collection process by comparing datasets and analyzing contrastive case studies.
Translation Quality Estimation by Jointly Learning to Score and Rank
Authors: Jingyi Zhang, Josef van Genabith
Date: 2020-11-01 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Multi-Task Learning, Sentence, Sentence Embeddings, Translation

The translation quality estimation (QE) task, particularly the QE as a Metric task, aims to evaluate the general quality of a translation based on the translation and the source sentence without using reference translations. Supervised learning of this QE task requires human evaluation of translation quality as training data. Human evaluation of translation quality can be performed in different ways, including assigning an absolute score to a translation or ranking different translations. In order to make use of different types of human evaluation data for supervised learning, we present a multi-task learning QE model that jointly learns two tasks: score a translation and rank two translations. Our QE model exploits cross-lingual sentence embeddings from pre-trained multilingual language models. We obtain new state-of-the-art results on the WMT 2019 QE as a Metric task and outperform sentBLEU on the WMT 2019 Metrics task.
Direct Segmentation Models for Streaming Speech Translation
Authors: Javier Iranzo-S{\'a}nchez, Adri{\`a} Gim{\'e}nez Pastor, Joan Albert Silvestre-Cerd{\`a}, Pau Baquero-Arnal, Jorge Civera Saiz, Alfons Juan
Date: 2020-11-01 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Machine Translation, Segmentation, speech-recognition, Speech Recognition, Translation

The cascade approach to Speech Translation (ST) is based on a pipeline that concatenates an Automatic Speech Recognition (ASR) system followed by a Machine Translation (MT) system. These systems are usually connected by a segmenter that splits the ASR output into hopefully, semantically self-contained chunks to be fed into the MT system. This is specially challenging in the case of streaming ST, where latency requirements must also be taken into account. This work proposes novel segmentation models for streaming ST that incorporate not only textual, but also acoustic information to decide when the ASR output is split into a chunk. An extensive and throughly experimental setup is carried out on the Europarl-ST dataset to prove the contribution of acoustic information to the performance of the segmentation model in terms of BLEU score in a streaming ST scenario. Finally, comparative results with previous work also show the superiority of the segmentation models proposed in this work.
CSP:Code-Switching Pre-training for Neural Machine Translation
Authors: Zhen Yang, Bojie Hu, Ambyera Han, Shen Huang, Qi Ju
Date: 2020-11-01 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Decoder, Machine Translation, NMT, Sentence, Translation

This paper proposes a new pre-training method, called Code-Switching Pre-training (CSP for short) for Neural Machine Translation (NMT). Unlike traditional pre-training method which randomly masks some fragments of the input sentence, the proposed CSP randomly replaces some words in the source sentence with their translation words in the target language. Specifically, we firstly perform lexicon induction with unsupervised word embedding mapping between the source and target languages, and then randomly replace some words in the input sentence with their translation words according to the extracted translation lexicons. CSP adopts the encoder-decoder framework: its encoder takes the code-mixed sentence as input, and its decoder predicts the replaced fragment of the input sentence. In this way, CSP is able to pre-train the NMT model by explicitly making the most of the alignment information extracted from the source and target monolingual corpus. Additionally, we relieve the pretrain-finetune discrepancy caused by the artificial symbols like [mask]. To verify the effectiveness of the proposed method, we conduct extensive experiments on unsupervised and supervised NMT. Experimental results show that CSP achieves significant improvements over baselines without pre-training or with other pre-training methods.
Losing Heads in the Lottery: Pruning Transformer Attention in Neural Machine Translation
Authors: Maximiliana Behnke, Kenneth Heafield
Date: 2020-11-01 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Machine Translation, Translation

The attention mechanism is the crucial component of the transformer architecture. Recent research shows that most attention heads are not confident in their decisions and can be pruned. However, removing them before training a model results in lower quality. In this paper, we apply the lottery ticket hypothesis to prune heads in the early stages of training. Our experiments on machine translation show that it is possible to remove up to three-quarters of attention heads from transformer-big during early training with an average -0.1 change in BLEU for Turkish→English. The pruned model is 1.5 times as fast at inference, albeit at the cost of longer training. Our method is complementary to other approaches, such as teacher-student, with English→German student model gaining an additional 10{\%} speed-up with 75{\%} encoder attention removed and 0.2 BLEU loss.
Towards Enhancing Faithfulness for Neural Machine Translation
Authors: Rongxiang Weng, Heng Yu, Xiangpeng Wei, Weihua Luo
Date: 2020-11-01 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Decoder, Machine Translation, Multi-Task Learning, NMT, Translation

Neural machine translation (NMT) has achieved great success due to the ability to generate high-quality sentences. Compared with human translations, one of the drawbacks of current NMT is that translations are not usually faithful to the input, e.g., omitting information or generating unrelated fragments, which inevitably decreases the overall quality, especially for human readers. In this paper, we propose a novel training strategy with a multi-task learning paradigm to build a faithfulness enhanced NMT model (named FEnmt). During the NMT training process, we sample a subset from the training set and translate them to get fragments that have been mistranslated. Afterward, the proposed multi-task learning paradigm is employed on both encoder and decoder to guide NMT to correctly translate these fragments. Both automatic and human evaluations verify that our FEnmt could improve translation quality by effectively reducing unfaithful translations.
Parsing Gapping Constructions Based on Grammatical and Semantic Roles
Authors: Yoshihide Kato, Shigeki Matsubara
Date: 2020-11-01 00:00:00
Proceeding: EMNLP 2020 11

A gapping construction consists of a coordinated structure where redundant elements are elided from all but one conjuncts. This paper proposes a method of parsing sentences with gapping to recover elided elements. The proposed method is based on constituent trees annotated with grammatical and semantic roles that are useful for identifying elided elements. Our method outperforms the previous method in terms of F-measure and recall.
Span-based discontinuous constituency parsing: a family of exact chart-based algorithms with time complexities from O(n\^6) down to O(n\^3)
Authors: Caio Corro
Date: 2020-11-01 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Constituency Parsing, Word Embeddings

We introduce a novel chart-based algorithm for span-based parsing of discontinuous constituency trees of block degree two, including ill-nested structures. In particular, we show that we can build variants of our parser with smaller search spaces and time complexities ranging from O(n{\^{}}6) down to O(n{\^{}}3). The cubic time variant covers 98{\%} of constituents observed in linguistic treebanks while having the same complexity as continuous constituency parsers. We evaluate our approach on German and English treebanks (Negra, Tiger, and DPTB) and report state-of-the-art results in the fully supervised setting. We also experiment with pre-trained word embeddings and Bert-based neural networks.
Some Languages Seem Easier to Parse Because Their Treebanks Leak
Authors: Anders S{\o}gaard
Date: 2020-11-01 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Dependency Parsing, Sentence

Cross-language differences in (universal) dependency parsing performance are mostly attributed to treebank size, average sentence length, average dependency length, morphological complexity, and domain differences. We point at a factor not previously discussed: If we abstract away from words and dependency labels, how many graphs in the test data were seen in the training data? We compute graph isomorphisms, and show that, treebank size aside, overlap between training and test graphs explain more of the observed variation than standard explanations such as the above.
Modularized Syntactic Neural Networks for Sentence Classification
Authors: Haiyan Wu, Ying Liu, Shaoyun Shi
Date: 2020-11-01 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Classification, General Classification, Sentence, Sentence Classification

This paper focuses on tree-based modeling for the sentence classification task. In existing works, aggregating on a syntax tree usually considers local information of sub-trees. In contrast, in addition to the local information, our proposed Modularized Syntactic Neural Network (MSNN) utilizes the syntax category labels and takes advantage of the global context while modeling sub-trees. In MSNN, each node of a syntax tree is modeled by a label-related syntax module. Each syntax module aggregates the outputs of lower-level modules, and finally, the root module provides the sentence representation. We design a tree-parallel mini-batch strategy for efficient training and predicting. Experimental results on four benchmark datasets show that our MSNN significantly outperforms previous state-of-the-art tree-based methods on the sentence classification task.
TED-CDB: A Large-Scale Chinese Discourse Relation Dataset on TED Talks
Authors: Wanqiu Long, Bonnie Webber, Deyi Xiong
Date: 2020-11-01 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Relation, Transfer Learning

As different genres are known to differ in their communicative properties and as previously, for Chinese, discourse relations have only been annotated over news text, we have created the TED-CDB dataset. TED-CDB comprises a large set of TED talks in Chinese that have been manually annotated according to the goals and principles of Penn Discourse Treebank, but adapted to features that are not present in English. It serves as a unique Chinese corpus of spoken discourse. Benchmark experiments show that TED-CDB poses a challenge for state-of-the-art discourse relation classifiers, whose F1 performance on 4-way classification is 60{\%}. This is a dramatic drop of 35{\%} from performance on the news text in the Chinese Discourse Treebank. Transfer learning experiments have been carried out with the TED-CDB for both same-language cross-domain transfer and same-domain cross-language transfer. Both demonstrate that the TED-CDB can improve the performance of systems being developed for languages other than Chinese and would be helpful for insufficient or unbalanced data in other corpora. The dataset and our Chinese annotation guidelines will be made freely available.
QADiscourse - Discourse Relations as QA Pairs: Representation, Crowdsourcing and Baselines
Authors: Valentina Pyatkin, Ayal Klein, Reut Tsarfaty, Ido Dagan
Date: 2020-11-01 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Natural Language Understanding, Sentence

Discourse relations describe how two propositions relate to one another, and identifying them automatically is an integral part of natural language understanding. However, annotating discourse relations typically requires expert annotators. Recently, different semantic aspects of a sentence have been represented and crowd-sourced via question-and-answer (QA) pairs. This paper proposes a novel representation of discourse relations as QA pairs, which in turn allows us to crowd-source wide-coverage data annotated with discourse relations, via an intuitively appealing interface for composing such questions and answers. Based on our proposed representation, we collect a novel and wide-coverage QADiscourse dataset, and present baseline algorithms for predicting QADiscourse relations.
Discourse Self-Attention for Discourse Element Identification in Argumentative Student Essays
Authors: Wei Song, Ziyao Song, Ruiji Fu, Lizhen Liu, Miaomiao Cheng, Ting Liu
Date: 2020-11-01 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Sentence

This paper proposes to adapt self-attention to discourse level for modeling discourse elements in argumentative student essays. Specifically, we focus on two issues. First, we propose structural sentence positional encodings to explicitly represent sentence positions. Second, we propose to use inter-sentence attentions to capture sentence interactions and enhance sentence representation. We conduct experiments on two datasets: a Chinese dataset and an English dataset. We find that (i) sentence positional encoding can lead to a large improvement for identifying discourse elements; (ii) a structural relative positional encoding of sentences shows to be most effective; (iii) inter-sentence attention vectors are useful as a kind of sentence representations for identifying discourse elements.
Improving Grammatical Error Correction Models with Purpose-Built Adversarial Examples
Authors: Lihao Wang, Xiaoqing Zheng
Date: 2020-11-01 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Grammatical Error Correction, Sentence

A sequence-to-sequence (seq2seq) learning with neural networks empirically shows to be an effective framework for grammatical error correction (GEC), which takes a sentence with errors as input and outputs the corrected one. However, the performance of GEC models with the seq2seq framework heavily relies on the size and quality of the corpus on hand. We propose a method inspired by adversarial training to generate more meaningful and valuable training examples by continually identifying the weak spots of a model, and to enhance the model by gradually adding the generated adversarial examples to the training set. Extensive experimental results show that such adversarial training can improve both the generalization and robustness of GEC models.
Homophonic Pun Generation with Lexically Constrained Rewriting
Authors: Zhiwei Yu, Hongyu Zang, Xiaojun Wan
Date: 2020-11-01 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Sentence

Punning is a creative way to make conversation enjoyable and literary writing elegant. In this paper, we focus on the task of generating a pun sentence given a pair of homophones. We first find the constraint words supporting the semantic incongruity for a sentence. Then we rewrite the sentence with explicit positive and negative constraints. Our model achieves the state-of-the-art results in both automatic and human evaluations. We further make an error analysis and discuss the challenges for the computational pun models.
How to Make Neural Natural Language Generation as Reliable as Templates in Task-Oriented Dialogue
Authors: Henry Elder, Alexander O{'}Connor, Jennifer Foster
Date: 2020-11-01 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Data Augmentation, Diversity, Text Generation

Neural Natural Language Generation (NLG) systems are well known for their unreliability. To overcome this issue, we propose a data augmentation approach which allows us to restrict the output of a network and guarantee reliability. While this restriction means generation will be less diverse than if randomly sampled, we include experiments that demonstrate the tendency of existing neural generation approaches to produce dull and repetitive text, and we argue that reliability is more important than diversity for this task. The system trained using this approach scored 100{\%} in semantic accuracy on the E2E NLG Challenge dataset, the same as a template system.
Slot Attention with Value Normalization for Multi-Domain Dialogue State Tracking
Authors: Yexiang Wang, Yi Guo, Siqi Zhu
Date: 2020-11-01 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Dialogue State Tracking, Multi-domain Dialogue State Tracking

Incompleteness of domain ontology and unavailability of some values are two inevitable problems of dialogue state tracking (DST). Existing approaches generally fall into two extremes: choosing models without ontology or embedding ontology in models leading to over-dependence. In this paper, we propose a new architecture to cleverly exploit ontology, which consists of Slot Attention (SA) and Value Normalization (VN), referred to as SAVN. Moreover, we supplement the annotation of supporting span for MultiWOZ 2.1, which is the shortest span in utterances to support the labeled value. SA shares knowledge between slots and utterances and only needs a simple structure to predict the supporting span. VN is designed specifically for the use of ontology, which can convert supporting spans to the values. Empirical results demonstrate that SAVN achieves the state-of-the-art joint accuracy of 54.52{\%} on MultiWOZ 2.0 and 54.86{\%} on MultiWOZ 2.1. Besides, we evaluate VN with incomplete ontology. The results show that even if only 30{\%} ontology is used, VN can also contribute to our model.
Scene Restoring for Narrative Machine Reading Comprehension
Authors: Zhixing Tian, Yuanzhe Zhang, Kang Liu, Jun Zhao, Yantao Jia, Zhicheng Sheng
Date: 2020-11-01 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Cloze Test, Machine Reading Comprehension, Multiple-choice, Reading Comprehension

This paper focuses on machine reading comprehension for narrative passages. Narrative passages usually describe a chain of events. When reading this kind of passage, humans tend to restore a scene according to the text with their prior knowledge, which helps them understand the passage comprehensively. Inspired by this behavior of humans, we propose a method to let the machine imagine a scene during reading narrative for better comprehension. Specifically, we build a scene graph by utilizing Atomic as the external knowledge and propose a novel Graph Dimensional-Iteration Network (GDIN) to encode the graph. We conduct experiments on the ROCStories, a dataset of Story Cloze Test (SCT), and CosmosQA, a dataset of multiple choice. Our method achieves state-of-the-art.
Conditional Causal Relationships between Emotions and Causes in Texts
Authors: Xinhong Chen, Qing Li, JianPing Wang
Date: 2020-11-01 00:00:00
Proceeding: EMNLP 2020 11
Tasks: valid

The causal relationships between emotions and causes in text have recently received a lot of attention. Most of the existing works focus on the extraction of the causally related clauses from documents. However, none of these works has considered the possibility that the causal relationships among the extracted emotion and cause clauses may only be valid under a specific context, without which the extracted clauses may not be causally related. To address such an issue, we propose a new task of determining whether or not an input pair of emotion and cause has a valid causal relationship under different contexts, and construct a corresponding dataset via manual annotation and negative sampling based on an existing benchmark dataset. Furthermore, we propose a prediction aggregation module with low computational overhead to fine-tune the prediction results based on the characteristics of the input clauses. Experiments demonstrate the effectiveness and generality of our aggregation module.
Cold-Start and Interpretability: Turning Regular Expressions into Trainable Recurrent Neural Networks
Authors: Chengyue Jiang, Yinggong Zhao, Shanbo Chu, Libin Shen, Kewei Tu
Date: 2020-11-01 00:00:00
Proceeding: EMNLP 2020 11
Tasks: text-classification, Text Classification

Neural networks can achieve impressive performance on many natural language processing applications, but they typically need large labeled data for training and are not easily interpretable. On the other hand, symbolic rules such as regular expressions are interpretable, require no training, and often achieve decent accuracy; but rules cannot benefit from labeled data when available and hence underperform neural networks in rich-resource scenarios. In this paper, we propose a type of recurrent neural networks called FA-RNNs that combine the advantages of neural networks and regular expression rules. An FA-RNN can be converted from regular expressions and deployed in zero-shot and cold-start scenarios. It can also utilize labeled data for training to achieve improved prediction accuracy. After training, an FA-RNN often remains interpretable and can be converted back into regular expressions. We apply FA-RNNs to text classification and observe that FA-RNNs significantly outperform previous neural approaches in both zero-shot and low-resource settings and remain very competitive in rich-resource settings.