pwc-archive/papers-with-abstracts huggingface dataset:
Integrating User History into Heterogeneous Graph for Dialogue Act Recognition
Authors: Dong Wang, Ziran Li, Haitao Zheng, Ying Shen
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Dialogue Act Classification, Diversity

Dialogue Act Recognition (DAR) is a challenging problem in Natural Language Understanding, which aims to attach Dialogue Act (DA) labels to each utterance in a conversation. However, previous studies cannot fully recognize the specific expressions given by users due to the informality and diversity of natural language expressions. To solve this problem, we propose a Heterogeneous User History (HUH) graph convolution network, which utilizes the user{'}s historical answers grouped by DA labels as additional clues to recognize the DA label of utterances. To handle the noise caused by introducing the user{'}s historical answers, we design sets of denoising mechanisms, including a History Selection process, a Similarity Re-weighting process, and an Edge Re-weighting process. We evaluate the proposed method on two benchmark datasets MSDialog and MRDA. The experimental results verify the effectiveness of integrating user{'}s historical answers, and show that our proposed model outperforms the state-of-the-art methods.
A Two-Level Interpretation of Modality in Human-Robot Dialogue
Authors: Lucia Donatelli, Kenneth Lai, James Pustejovsky
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Vocal Bursts Valence Prediction

We analyze the use and interpretation of modal expressions in a corpus of situated human-robot dialogue and ask how to effectively represent these expressions for automatic learning. We present a two-level annotation scheme for modality that captures both content and intent, integrating a logic-based, semantic representation and a task-oriented, pragmatic representation that maps to our robot{'}s capabilities. Data from our annotation task reveals that the interpretation of modal expressions in human-robot dialogue is quite diverse, yet highly constrained by the physical environment and asymmetrical speaker/addressee relationship. We sketch a formal model of human-robot common ground in which modality can be grounded and dynamically interpreted.
Improving Low-Resource NMT through Relevance Based Linguistic Features Incorporation
Authors: Abhisek Chakrabarty, Raj Dabre, Chenchen Ding, Masao Utiyama, Eiichiro Sumita
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Low Resource NMT, Machine Translation, NMT, Translation

In this study, linguistic knowledge at different levels are incorporated into the neural machine translation (NMT) framework to improve translation quality for language pairs with extremely limited data. Integrating manually designed or automatically extracted features into the NMT framework is known to be beneficial. However, this study emphasizes that the relevance of the features is crucial to the performance. Specifically, we propose two methods, 1) self relevance and 2) word-based relevance, to improve the representation of features for NMT. Experiments are conducted on translation tasks from English to eight Asian languages, with no more than twenty thousand sentences for training. The proposed methods improve translation quality for all tasks by up to 3.09 BLEU points. Discussions with visualization provide the explainability of the proposed methods where we show that the relevance methods provide weights to features thereby enhancing their impact on low-resource machine translation.
Bilingual Subword Segmentation for Neural Machine Translation
Authors: Hiroyuki Deguchi, Masao Utiyama, Akihiro Tamura, Takashi Ninomiya, Eiichiro Sumita
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Machine Translation, Segmentation, Sentence, Translation

This paper proposed a new subword segmentation method for neural machine translation, {``}Bilingual Subword Segmentation,{''} which tokenizes sentences to minimize the difference between the number of subword units in a sentence and that of its translation. While existing subword segmentation methods tokenize a sentence without considering its translation, the proposed method tokenizes a sentence by using subword units induced from bilingual sentences; this method could be more favorable to machine translation. Evaluations on WAT Asian Scientific Paper Excerpt Corpus (ASPEC) English-to-Japanese and Japanese-to-English translation tasks and WMT14 English-to-German and German-to-English translation tasks show that our bilingual subword segmentation improves the performance of Transformer neural machine translation (up to +0.81 BLEU).
Supervised Visual Attention for Multimodal Neural Machine Translation
Authors: Tetsuro Nishihara, Akihiro Tamura, Takashi Ninomiya, Yutaro Omote, Hideki Nakayama
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Machine Translation, Sentence, Translation

This paper proposed a supervised visual attention mechanism for multimodal neural machine translation (MNMT), trained with constraints based on manual alignments between words in a sentence and their corresponding regions of an image. The proposed visual attention mechanism captures the relationship between a word and an image region more precisely than a conventional visual attention mechanism trained through MNMT in an unsupervised manner. Our experiments on English-German and German-English translation tasks using the Multi30k dataset and on English-Japanese and Japanese-English translation tasks using the Flickr30k Entities JP dataset show that a Transformer-based MNMT model can be improved by incorporating our proposed supervised visual attention mechanism and that further improvements can be achieved by combining it with a supervised cross-lingual attention mechanism (up to +1.61 BLEU, +1.7 METEOR).
The Two Shades of Dubbing in Neural Machine Translation
Authors: Alina Karakanta, Supratik Bhattacharya, Shravan Nayak, Timo Baumann, Matteo Negri, Marco Turchi
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Machine Translation, Translation, Vocal Bursts Valence Prediction

Dubbing has two shades; synchronisation constraints are applied only when the actor{'}s mouth is visible on screen, while the translation is unconstrained for off-screen dubbing. Consequently, different synchronisation requirements, and therefore translation strategies, are applied depending on the type of dubbing. In this work, we manually annotate an existing dubbing corpus (Heroes) for this dichotomy. We show that, even though we did not observe distinctive features between on- and off-screen dubbing at the textual level, on-screen dubbing is more difficult for MT (-4 BLEU points). Moreover, synchronisation constraints dramatically decrease translation quality for off-screen dubbing. We conclude that, distinguishing between on-screen and off-screen dubbing is necessary for determining successful strategies for dubbing-customised Machine Translation.
Filtering Back-Translated Data in Unsupervised Neural Machine Translation
Authors: Jyotsana Khatri, Pushpak Bhattacharyya
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Domain Adaptation, Machine Translation, NMT, Sentence, Translation

Unsupervised neural machine translation (NMT) utilizes only monolingual data for training. The quality of back-translated data plays an important role in the performance of NMT systems. In back-translation, all generated pseudo parallel sentence pairs are not of the same quality. Taking inspiration from domain adaptation where in-domain sentences are given more weight in training, in this paper we propose an approach to filter back-translated data as part of the training process of unsupervised NMT. Our approach gives more weight to good pseudo parallel sentence pairs in the back-translation phase. We calculate the weight of each pseudo parallel sentence pair using sentence-wise round-trip BLEU score which is normalized batch-wise. We compare our approach with the current state of the art approaches for unsupervised NMT.
Lost in Back-Translation: Emotion Preservation in Neural Machine Translation
Authors: Enrica Troiano, Roman Klinger, Sebastian Pad{\'o}
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Diversity, Machine Translation, Re-Ranking, Style Transfer, Translation

Machine translation provides powerful methods to convert text between languages, and is therefore a technology enabling a multilingual world. An important part of communication, however, takes place at the non-propositional level (e.g., politeness, formality, emotions), and it is far from clear whether current MT methods properly translate this information. This paper investigates the specific hypothesis that the non-propositional level of emotions is at least partially lost in MT. We carry out a number of experiments in a back-translation setup and establish that (1) emotions are indeed partially lost during translation; (2) this tendency can be reversed almost completely with a simple re-ranking approach informed by an emotion classifier, taking advantage of diversity in the n-best list; (3) the re-ranking approach can also be applied to change emotions, obtaining a model for emotion style transfer. An in-depth qualitative analysis reveals that there are recurring linguistic changes through which emotions are toned down or amplified, such as change of modality.
Intermediate Self-supervised Learning for Machine Translation Quality Estimation
Authors: Raphael Rubino, Eiichiro Sumita
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Domain Adaptation, Language Modeling, Language Modelling, Machine Translation, Self-Supervised Learning, Sentence, Translation

Pre-training sentence encoders is effective in many natural language processing tasks including machine translation (MT) quality estimation (QE), due partly to the scarcity of annotated QE data required for supervised learning. In this paper, we investigate the use of an intermediate self-supervised learning task for sentence encoder aiming at improving QE performances at the sentence and word levels. Our approach is motivated by a problem inherent to QE: mistakes in translation caused by wrongly inserted and deleted tokens. We modify the translation language model (TLM) training objective of the cross-lingual language model (XLM) to orientate the pre-trained model towards the target task. The proposed method does not rely on annotated data and is complementary to QE methods involving pre-trained sentence encoders and domain adaptation. Experiments on English-to-German and English-to-Russian translation directions show that intermediate learning improves over domain adaptated models. Additionally, our method reaches results in par with state-of-the-art QE models without requiring the combination of several approaches and outperforms similar methods based on pre-trained sentence encoders.
Unifying Input and Output Smoothing in Neural Machine Translation
Authors: Yingbo Gao, Baohao Liao, Hermann Ney
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Data Augmentation, Language Modeling, Language Modelling, Machine Translation, Translation

Soft contextualized data augmentation is a recent method that replaces one-hot representation of words with soft posterior distributions of an external language model, smoothing the input of neural machine translation systems. Label smoothing is another effective method that penalizes over-confident model outputs by discounting some probability mass from the true target word, smoothing the output of neural machine translation systems. Having the benefit of updating all word vectors in each optimization step and better regularizing the models, the two smoothing methods are shown to bring significant improvements in translation performance. In this work, we study how to best combine the methods and stack the improvements. Specifically, we vary the prior distributions to smooth with, the hyperparameters that control the smoothing strength, and the token selection procedures. We conduct extensive experiments on small datasets, evaluate the recipes on larger datasets, and examine the implications when back-translation is further used. Our results confirm cumulative improvements when input and output smoothing are used in combination, giving up to +1.9 BLEU scores on standard machine translation tasks and reveal reasons why these smoothing methods should be preferred.
Neural Transduction for Multilingual Lexical Translation
Authors: Dylan Lewis, Winston Wu, Arya D. McCarthy, David Yarowsky
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Mixture-of-Experts, Translation

We present a method for completing multilingual translation dictionaries. Our probabilistic approach can synthesize new word forms, allowing it to operate in settings where correct translations have not been observed in text (cf. cross-lingual embeddings). In addition, we propose an approximate Maximum Mutual Information (MMI) decoding objective to further improve performance in both many-to-one and one-to-one word level translation tasks where we use either multiple input languages for a single target language or more typical single language pair translation. The model is trained in a many-to-many setting, where it can leverage information from related languages to predict words in each of its many target languages. We focus on 6 languages: French, Spanish, Italian, Portuguese, Romanian, and Turkish. When indirect multilingual information is available, ensembling with mixture-of-experts as well as incorporating related languages leads to a 27{\%} relative improvement in whole-word accuracy of predictions over a single-source baseline. To seed the completion when multilingual data is unavailable, it is better to decode with an MMI objective.
A Document-Level Neural Machine Translation Model with Dynamic Caching Guided by Theme-Rheme Information
Authors: Yiqi Tong, Jiangbin Zheng, Hongkang Zhu, Yidong Chen, Xiaodong Shi
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Machine Translation, NMT, Sentence, Translation

Research on document-level Neural Machine Translation (NMT) models has attracted increasing attention in recent years. Although the proposed works have proved that the inter-sentence information is helpful for improving the performance of the NMT models, what information should be regarded as context remains ambiguous. To solve this problem, we proposed a novel cache-based document-level NMT model which conducts dynamic caching guided by theme-rheme information. The experiments on NIST evaluation sets demonstrate that our proposed model achieves substantial improvements over the state-of-the-art baseline NMT models. As far as we know, we are the first to introduce theme-rheme theory into the field of machine translation.
Combining Cognitive Modeling and Reinforcement Learning for Clarification in Dialogue
Authors: Baber Khalid, Malihe Alikhani, Matthew Stone
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In many domains, dialogue systems need to work collaboratively with users to successfully reconstruct the meaning the user had in mind. In this paper, we show how cognitive models of users{'} communicative strategies can be leveraged in a reinforcement learning approach to dialogue planning to enable interactive systems to give targeted, effective feedback about the system{'}s understanding. We describe a prototype system that collaborates on reference tasks that distinguish arbitrarily varying color patches from similar distractors, and use experiments with crowd workers and analyses of our learned policies to document that our approach leads to context-sensitive clarification strategies that focus on key missing information, elicit correct answers that the system understands, and contribute to increasing dialogue success.
Knowledge Aware Emotion Recognition in Textual Conversations via Multi-Task Incremental Transformer
Authors: Duzhen Zhang, Xiuyi Chen, Shuang Xu, Bo Xu
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Emotion Recognition, Graph Attention, Multi-Task Learning, Opinion Mining, Recommendation Systems

Emotion recognition in textual conversations (ERTC) plays an important role in a wide range of applications, such as opinion mining, recommender systems, and so on. ERTC, however, is a challenging task. For one thing, speakers often rely on the context and commonsense knowledge to express emotions; for another, most utterances contain neutral emotion in conversations, as a result, the confusion between a few non-neutral utterances and much more neutral ones restrains the emotion recognition performance. In this paper, we propose a novel Knowledge Aware Incremental Transformer with Multi-task Learning (KAITML) to address these challenges. Firstly, we devise a dual-level graph attention mechanism to leverage commonsense knowledge, which augments the semantic information of the utterance. Then we apply the Incremental Transformer to encode multi-turn contextual utterances. Moreover, we are the first to introduce multi-task learning to alleviate the aforementioned confusion and thus further improve the emotion recognition performance. Extensive experimental results show that our KAITML model outperforms the state-of-the-art models across five benchmark datasets.
MEISD: A Multimodal Multi-Label Emotion, Intensity and Sentiment Dialogue Dataset for Emotion Recognition and Sentiment Analysis in Conversations
Authors: Mauajama Firdaus, Hardik Chauhan, Asif Ekbal, Pushpak Bhattacharyya
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Emotion Recognition, Sentiment Analysis, Sentiment Classification

Emotion and sentiment classification in dialogues is a challenging task that has gained popularity in recent times. Humans tend to have multiple emotions with varying intensities while expressing their thoughts and feelings. Emotions in an utterance of dialogue can either be independent or dependent on the previous utterances, thus making the task complex and interesting. Multi-label emotion detection in conversations is a significant task that provides the ability to the system to understand the various emotions of the users interacting. Sentiment analysis in dialogue/conversation, on the other hand, helps in understanding the perspective of the user with respect to the ongoing conversation. Along with text, additional information in the form of audio and video assist in identifying the correct emotions with the appropriate intensity and sentiments in an utterance of a dialogue. Lately, quite a few datasets have been made available for dialogue emotion and sentiment classification, but these datasets are imbalanced in representing different emotions and consist of an only single emotion. Hence, we present at first a large-scale balanced Multimodal Multi-label Emotion, Intensity, and Sentiment Dialogue dataset (MEISD), collected from different TV series that has textual, audio and visual features, and then establish a baseline setup for further research.
EmpDG: Multi-resolution Interactive Empathetic Dialogue Generation
Authors: Qintong Li, Hongshen Chen, Zhaochun Ren, Pengjie Ren, Zhaopeng Tu, Zhumin Chen
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Dialogue Generation

A humanized dialogue system is expected to generate empathetic replies, which should be sensitive to the users{'} expressed emotion. The task of empathetic dialogue generation is proposed to address this problem. The essential challenges lie in accurately capturing the nuances of human emotion and considering the potential of user feedback, which are overlooked by the majority of existing work. In response to this problem, we propose a multi-resolution adversarial model {--} EmpDG, to generate more empathetic responses. EmpDG exploits both the coarse-grained dialogue-level and fine-grained token-level emotions, the latter of which helps to better capture the nuances of user emotion. In addition, we introduce an interactive adversarial learning framework which exploits the user feedback, to identify whether the generated responses evoke emotion perceptivity in dialogues. Experimental results show that the proposed approach significantly outperforms the state-of-the-art baselines in both content quality and emotion perceptivity.
Effective Use of Target-side Context for Neural Machine Translation
Authors: Hideya Mino, Hitoshi Ito, Isao Goto, Ichiro Yamada, Takenobu Tokunaga
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Articles, Domain Adaptation, Machine Translation, NMT, Translation

In this paper, we deal with two problems in Japanese-English machine translation of news articles. The first problem is the quality of parallel corpora. Neural machine translation (NMT) systems suffer degraded performance when trained with noisy data. Because there is no clean Japanese-English parallel data for news articles, we build a novel parallel news corpus consisting of Japanese news articles translated into English in a content-equivalent manner. This is the first content-equivalent Japanese-English news corpus translated specifically for training NMT systems. The second problem involves the domain-adaptation technique. NMT systems suffer degraded performance when trained with mixed data having different features, such as noisy data and clean data. Though the existing methods try to overcome this problem by using tags for distinguishing the differences between corpora, it is not sufficient. We thus extend a domain-adaptation method using multi-tags to train an NMT model effectively with the clean corpus and existing parallel news corpora with some types of noise. Experimental results show that our corpus increases the translation quality, and that our domain-adaptation method is more effective for learning with the multiple types of corpora than existing domain-adaptation methods are.
Knowledge Graph Enhanced Neural Machine Translation via Multi-task Learning on Sub-entity Granularity
Authors: Yang Zhao, Lu Xiang, Junnan Zhu, Jiajun Zhang, Yu Zhou, Chengqing Zong
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Machine Translation, Multi-Task Learning, NMT, Sentence, Translation

Previous studies combining knowledge graph (KG) with neural machine translation (NMT) have two problems: i) Knowledge under-utilization: they only focus on the entities that appear in both KG and training sentence pairs, making much knowledge in KG unable to be fully utilized. ii) Granularity mismatch: the current KG methods utilize the entity as the basic granularity, while NMT utilizes the sub-word as the granularity, making the KG different to be utilized in NMT. To alleviate above problems, we propose a multi-task learning method on sub-entity granularity. Specifically, we first split the entities in KG and sentence pairs into sub-entity granularity by using joint BPE. Then we utilize the multi-task learning to combine the machine translation task and knowledge reasoning task. The extensive experiments on various translation tasks have demonstrated that our method significantly outperforms the baseline models in both translation quality and handling the entities.