pwc-archive/papers-with-abstracts huggingface dataset:
Active Reading Comprehension: A Dataset for Learning the Question-Answer Relationship Strategy
Authors: Diana Galv{\'a}n-Sosa
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Question Answering, Reading Comprehension

Reading comprehension (RC) through question answering is a useful method for evaluating if a reader understands a text. Standard accuracy metrics are used for evaluation, where high accuracy is taken as indicative of a good understanding. However, literature in quality learning suggests that task performance should also be evaluated on the undergone process to answer. The Question-Answer Relationship (QAR) is one of the strategies for evaluating a reader{'}s understanding based on their ability to select different sources of information depending on the question type. We propose the creation of a dataset to learn the QAR strategy with weak supervision. We expect to complement current work on reading comprehension by introducing a new setup for evaluation.
Improving Mongolian-Chinese Neural Machine Translation with Morphological Noise
Authors: Yatu Ji, Hongxu Hou, Chen Junjie, Nier Wu
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Machine Translation, Translation

For the translation of agglutinative language such as typical Mongolian, unknown (UNK) words not only come from the quite restricted vocabulary, but also mostly from misunderstanding of the translation model to the morphological changes. In this study, we introduce a new adversarial training model to alleviate the UNK problem in Mongolian-Chinese machine translation. The training process can be described as three adversarial sub models (generator, value screener and discriminator), playing a win-win game. In this game, the added screener plays the role of emphasizing that the discriminator pays attention to the added Mongolian morphological noise in the form of pseudo-data and improving the training efficiency. The experimental results show that the newly emerged Mongolian-Chinese task is state-of-the-art. Under this premise, the training time is greatly shortened.
Unsupervised Pretraining for Neural Machine Translation Using Elastic Weight Consolidation
Authors: Du{\v{s}}an Vari{\v{s}}, Ond{\v{r}}ej Bojar
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Decoder, Language Modeling, Language Modelling, Machine Translation, NMT, Translation

This work presents our ongoing research of unsupervised pretraining in neural machine translation (NMT). In our method, we initialize the weights of the encoder and decoder with two language models that are trained with monolingual data and then fine-tune the model on parallel data using Elastic Weight Consolidation (EWC) to avoid forgetting of the original language modeling task. We compare the regularization by EWC with the previous work that focuses on regularization by language modeling objectives. The positive result is that using EWC with the decoder achieves BLEU scores similar to the previous work. However, the model converges 2-3 times faster and does not require the original unlabeled training data during the fine-tuning stage. In contrast, the regularization using EWC is less effective if the original and new tasks are not closely related. We show that initializing the bidirectional NMT encoder with a left-to-right language model and forcing the model to remember the original left-to-right language modeling task limits the learning capacity of the encoder for the whole bidirectional context.
M\=aOri Loanwords: A Corpus of New Zealand English Tweets
Authors: David Trye, Andreea Calude, Felipe Bravo-Marquez, Te Taka Keegan
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7

M{\=a}ori loanwords are widely used in New Zealand English for various social functions by New Zealanders within and outside of the M{\=a}ori community. Motivated by the lack of linguistic resources for studying how M{\=a}ori loanwords are used in social media, we present a new corpus of New Zealand English tweets. We collected tweets containing selected M{\=a}ori words that are likely to be known by New Zealanders who do not speak M{\=a}ori. Since over 30{\%} of these words turned out to be irrelevant, we manually annotated a sample of our tweets into relevant and irrelevant categories. This data was used to train machine learning models to automatically filter out irrelevant tweets.
Ranking of Potential Questions
Authors: Luise Schricker, Tatjana Scheffler
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7

Questions are an integral part of discourse. They provide structure and support the exchange of information. One linguistic theory, the Questions Under Discussion model, takes question structures as integral to the functioning of a coherent discourse. This theory has not been tested on the count of its validity for predicting observations in real dialogue data, however. In this submission, a system for ranking explicit and implicit questions by their appropriateness in a dialogue is presented. This system implements constraints and principles put forward in the linguistic literature.
Controlling Grammatical Error Correction Using Word Edit Rate
Authors: Kengo Hotate, Masahiro Kaneko, Satoru Katsumata, Mamoru Komachi
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Grammatical Error Correction, Sentence

When professional English teachers correct grammatically erroneous sentences written by English learners, they use various methods. The correction method depends on how much corrections a learner requires. In this paper, we propose a method for neural grammar error correction (GEC) that can control the degree of correction. We show that it is possible to actually control the degree of GEC by using new training data annotated with word edit rate. Thereby, diverse corrected sentences is obtained from a single erroneous sentence. Moreover, compared to a GEC model that does not use information on the degree of correction, the proposed method improves correction accuracy.
From Brain Space to Distributional Space: The Perilous Journeys of fMRI Decoding
Authors: Gosse Minnema, Aur{\'e}lie Herbelot
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Decoder

Recent work in cognitive neuroscience has introduced models for predicting distributional word meaning representations from brain imaging data. Such models have great potential, but the quality of their predictions has not yet been thoroughly evaluated from a computational linguistics point of view. Due to the limited size of available brain imaging datasets, standard quality metrics (e.g. similarity judgments and analogies) cannot be used. Instead, we investigate the use of several alternative measures for evaluating the predicted distributional space against a corpus-derived distributional space. We show that a state-of-the-art decoder, while performing impressively on metrics that are commonly used in cognitive neuroscience, performs unexpectedly poorly on our metrics. To address this, we propose strategies for improving the model{'}s performance. Despite returning promising results, our experiments also demonstrate that much work remains to be done before distributional representations can reliably be predicted from brain data.
Towards Incremental Learning of Word Embeddings Using Context Informativeness
Authors: Alex Kabbach, re, Kristina Gulordava, Aur{\'e}lie Herbelot
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Incremental Learning, Informativeness, Learning Word Embeddings, Word Embeddings

In this paper, we investigate the task of learning word embeddings from very sparse data in an incremental, cognitively-plausible way. We focus on the notion of {`}informativeness{'}, that is, the idea that some content is more valuable to the learning process than other. We further highlight the challenges of online learning and argue that previous systems fall short of implementing incrementality. Concretely, we incorporate informativeness in a previously proposed model of nonce learning, using it for context selection and learning rate modulation. We test our system on the task of learning new words from definitions, as well as on the task of learning new words from potentially uninformative contexts. We demonstrate that informativeness is crucial to obtaining state-of-the-art performance in a truly incremental setup.
Incorporating Textual Information on User Behavior for Personality Prediction
Authors: Kosuke Yamada, Ryohei Sasano, Koichi Takeda
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Prediction

Several recent studies have shown that textual information of user posts and user behaviors such as liking and sharing the specific posts are useful for predicting the personality of social media users. However, less attention has been paid to the textual information derived from the user behaviors. In this paper, we investigate the effect of textual information on user behaviors for personality prediction. Our experiments on the personality prediction of Twitter users show that the textual information of user behaviors is more useful than the co-occurrence information of the user behaviors. They also show that taking user behaviors into account is crucial for predicting the personality of users who do not post frequently.
Corpus Creation and Analysis for Named Entity Recognition in Telugu-English Code-Mixed Social Media Data
Authors: Vamshi Krishna Srirangam, Appidi Abhinav Reddy, Vinay Singh, Manish Shrivastava
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Entity Extraction using GAN, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Sentence, TAG

Named Entity Recognition(NER) is one of the important tasks in Natural Language Processing(NLP) and also is a subtask of Information Extraction. In this paper we present our work on NER in Telugu-English code-mixed social media data. Code-Mixing, a progeny of multilingualism is a way in which multilingual people express themselves on social media by using linguistics units from different languages within a sentence or speech context. Entity Extraction from social media data such as tweets(twitter) is in general difficult due to its informal nature, code-mixed data further complicates the problem due to its informal, unstructured and incomplete information. We present a Telugu-English code-mixed corpus with the corresponding named entity tags. The named entities used to tag data are Person({`}Per{'}), Organization({`}Org{'}) and Location({`}Loc{'}). We experimented with the machine learning models Conditional Random Fields(CRFs), Decision Trees and BiLSTMs on our corpus which resulted in a F1-score of 0.96, 0.94 and 0.95 respectively.
Dialogue-Act Prediction of Future Responses Based on Conversation History
Authors: Koji Tanaka, Junya Takayama, Yuki Arase
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Chatbot, Response Generation

Sequence-to-sequence models are a common approach to develop a chatbot. They can train a conversational model in an end-to-end manner. One significant drawback of such a neural network based approach is that the response generation process is a black-box, and how a specific response is generated is unclear. To tackle this problem, an interpretable response generation mechanism is desired. As a step toward this direction, we focus on dialogue-acts (DAs) that may provide insight to understand the response generation process. In particular, we propose a method to predict a DA of the next response based on the history of previous utterances and their DAs. Experiments using a Switch Board Dialogue Act corpus show that compared to the baseline considering only a single utterance, our model achieves 10.8{\%} higher F1-score and 3.0{\%} higher accuracy on DA prediction.
Computational Ad Hominem Detection
Authors: Pieter Delobelle, Murilo Cunha, Eric Massip Cano, Jeroen Peperkamp, Bettina Berendt
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: BIG-bench Machine Learning

Fallacies like the personal attack{---}also known as the ad hominem attack{---}are introduced in debates as an easy win, even though they provide no rhetorical contribution. Although their importance in argumentation mining is acknowledged, automated mining and analysis is still lacking. We show TF-IDF approaches are insufficient to detect the ad hominem attack. Therefore we present a machine learning approach for information extraction, which has a recall of 80{\%} for a social media data source. We also demonstrate our approach with an application that uses online learning.
Attention over Heads: A Multi-Hop Attention for Neural Machine Translation
Authors: Shohei Iida, Ryuichiro Kimura, Hongyi Cui, Po-Hsuan Hung, Takehito Utsuro, Masaaki Nagata
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Machine Translation, Translation

In this paper, we propose a multi-hop attention for the Transformer. It refines the attention for an output symbol by integrating that of each head, and consists of two hops. The first hop attention is the scaled dot-product attention which is the same attention mechanism used in the original Transformer. The second hop attention is a combination of multi-layer perceptron (MLP) attention and head gate, which efficiently increases the complexity of the model by adding dependencies between heads. We demonstrate that the translation accuracy of the proposed multi-hop attention outperforms the baseline Transformer significantly, +0.85 BLEU point for the IWSLT-2017 German-to-English task and +2.58 BLEU point for the WMT-2017 German-to-English task. We also find that the number of parameters required for a multi-hop attention is smaller than that for stacking another self-attention layer and the proposed model converges significantly faster than the original Transformer.
Attention and Lexicon Regularized LSTM for Aspect-based Sentiment Analysis
Authors: Lingxian Bao, Patrik Lambert, Toni Badia
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Sentence, Sentiment Analysis

Abstract Attention based deep learning systems have been demonstrated to be the state of the art approach for aspect-level sentiment analysis, however, end-to-end deep neural networks lack flexibility as one can not easily adjust the network to fix an obvious problem, especially when more training data is not available: e.g. when it always predicts \textit{positive} when seeing the word \textit{disappointed}. Meanwhile, it is less stressed that attention mechanism is likely to {``}over-focus{''} on particular parts of a sentence, while ignoring positions which provide key information for judging the polarity. In this paper, we describe a simple yet effective approach to leverage lexicon information so that the model becomes more flexible and robust. We also explore the effect of regularizing attention vectors to allow the network to have a broader {``}focus{''} on different parts of the sentence. The experimental results demonstrate the effectiveness of our approach.
Controllable Text Simplification with Lexical Constraint Loss
Authors: Daiki Nishihara, Tomoyuki Kajiwara, Yuki Arase
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Sentence, Text Simplification, Translation

We propose a method to control the level of a sentence in a text simplification task. Text simplification is a monolingual translation task translating a complex sentence into a simpler and easier to understand the alternative. In this study, we use the grade level of the US education system as the level of the sentence. Our text simplification method succeeds in translating an input into a specific grade level by considering levels of both sentences and words. Sentence level is considered by adding the target grade level as input. By contrast, the word level is considered by adding weights to the training loss based on words that frequently appear in sentences of the desired grade level. Although existing models that consider only the sentence level may control the syntactic complexity, they tend to generate words beyond the target level. Our approach can control both the lexical and syntactic complexity and achieve an aggressive rewriting. Experiment results indicate that the proposed method improves the metrics of both BLEU and SARI.
Normalizing Non-canonical Turkish Texts Using Machine Translation Approaches
Authors: Talha {\c{C}}olako{\u{g}}lu, Umut Sulubacak, Ahmet C{\"u}neyd Tantu{\u{g}}
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Machine Translation, Text Normalization, Translation

With the growth of the social web, user-generated text data has reached unprecedented sizes. Non-canonical text normalization provides a way to exploit this as a practical source of training data for language processing systems. The state of the art in Turkish text normalization is composed of a token level pipeline of modules, heavily dependent on external linguistic resources and manually defined rules. Instead, we propose a fully automated, context-aware machine translation approach with fewer stages of processing. Experiments with various implementations of our approach show that we are able to surpass the current best-performing system by a large margin.
ARHNet - Leveraging Community Interaction for Detection of Religious Hate Speech in Arabic
Authors: Arijit Ghosh Chowdhury, Aniket Didolkar, Ramit Sawhney, Rajiv Ratn Shah
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Word Embeddings

The rapid widespread of social media has lead to some undesirable consequences like the rapid increase of hateful content and offensive language. Religious Hate Speech, in particular, often leads to unrest and sometimes aggravates to violence against people on the basis of their religious affiliations. The richness of the Arabic morphology and the limited available resources makes this task especially challenging. The current state-of-the-art approaches to detect hate speech in Arabic rely entirely on textual (lexical and semantic) cues. Our proposed methodology contends that leveraging Community-Interaction can better help us profile hate speech content on social media. Our proposed ARHNet (Arabic Religious Hate Speech Net) model incorporates both Arabic Word Embeddings and Social Network Graphs for the detection of religious hate speech.
Investigating Political Herd Mentality: A Community Sentiment Based Approach
Authors: Anjali Bhavan, Rohan Mishra, Pradyumna Prakhar Sinha, Ramit Sawhney, Rajiv Ratn Shah
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: General Classification, Sentiment Analysis, Sentiment Classification

Analyzing polarities and sentiments inherent in political speeches and debates poses an important problem today. This experiment aims to address this issue by analyzing publicly-available Hansard transcripts of the debates conducted in the UK Parliament. Our proposed approach, which uses community-based graph information to augment hand-crafted features based on topic modeling and emotion detection on debate transcripts, currently surpasses the benchmark results on the same dataset. Such sentiment classification systems could prove to be of great use in today{'}s politically turbulent times, for public knowledge of politicians{'} stands on various relevant issues proves vital for good governance and citizenship. The experiments also demonstrate that continuous feature representations learned from graphs can improve performance on sentiment classification tasks significantly.
Transfer Learning Based Free-Form Speech Command Classification for Low-Resource Languages
Authors: Yohan Karunanayake, Uthayasanker Thayasivam, Surangika Ranathunga
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Form, General Classification, Transfer Learning

Current state-of-the-art speech-based user interfaces use data intense methodologies to recognize free-form speech commands. However, this is not viable for low-resource languages, which lack speech data. This restricts the usability of such interfaces to a limited number of languages. In this paper, we propose a methodology to develop a robust domain-specific speech command classification system for low-resource languages using speech data of a high-resource language. In this transfer learning-based approach, we used a Convolution Neural Network (CNN) to identify a fixed set of intents using an ASR-based character probability map. We were able to achieve significant results for Sinhala and Tamil datasets using an English based ASR, which attests the robustness of the proposed approach.
Embedding Strategies for Specialized Domains: Application to Clinical Entity Recognition
Authors: Hicham El Boukkouri, Olivier Ferret, Thomas Lavergne, Pierre Zweigenbaum
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Clinical Concept Extraction, Word Embeddings

Using pre-trained word embeddings in conjunction with Deep Learning models has become the {``}de facto{''} approach in Natural Language Processing (NLP). While this usually yields satisfactory results, off-the-shelf word embeddings tend to perform poorly on texts from specialized domains such as clinical reports. Moreover, training specialized word representations from scratch is often either impossible or ineffective due to the lack of large enough in-domain data. In this work, we focus on the clinical domain for which we study embedding strategies that rely on general-domain resources only. We show that by combining off-the-shelf contextual embeddings (ELMo) with static word2vec embeddings trained on a small in-domain corpus built from the task data, we manage to reach and sometimes outperform representations learned from a large corpus in the medical domain.
English-Indonesian Neural Machine Translation for Spoken Language Domains
Authors: Meisyarah Dwiastuti
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Domain Adaptation, Machine Translation, NMT, Translation

In this work, we conduct a study on Neural Machine Translation (NMT) for English-Indonesian (EN-ID) and Indonesian-English (ID-EN). We focus on spoken language domains, namely colloquial and speech languages. We build NMT systems using the Transformer model for both translation directions and implement domain adaptation, in which we train our pre-trained NMT systems on speech language (in-domain) data. Moreover, we conduct an evaluation on how the domain-adaptation method in our EN-ID system can result in more formal translation outputs.
Improving Neural Entity Disambiguation with Graph Embeddings
Authors: {\"O}zge Sevgili, Alex Panchenko, er, Chris Biemann
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Articles, Entity Disambiguation

Entity Disambiguation (ED) is the task of linking an ambiguous entity mention to a corresponding entry in a knowledge base. Current methods have mostly focused on unstructured text data to learn representations of entities, however, there is structured information in the knowledge base itself that should be useful to disambiguate entities. In this work, we propose a method that uses graph embeddings for integrating structured information from the knowledge base with unstructured information from text-based representations. Our experiments confirm that graph embeddings trained on a graph of hyperlinks between Wikipedia articles improve the performances of simple feed-forward neural ED model and a state-of-the-art neural ED system.