pwc-archive/papers-with-abstracts huggingface dataset:
Fully Statistical Neural Belief Tracking
Authors: Nikola Mrk{\v{s}}i{\'c}, Ivan Vuli{\'c}
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Dialogue Management, Dialogue State Tracking, Spoken Language Understanding, Word Embeddings

This paper proposes an improvement to the existing data-driven Neural Belief Tracking (NBT) framework for Dialogue State Tracking (DST). The existing NBT model uses a hand-crafted belief state update mechanism which involves an expensive manual retuning step whenever the model is deployed to a new dialogue domain. We show that this update mechanism can be learned jointly with the semantic decoding and context modelling parts of the NBT model, eliminating the last rule-based module from this DST framework. We propose two different statistical update mechanisms and show that dialogue dynamics can be modelled with a very small number of additional model parameters. In our DST evaluation over three languages, we show that this model achieves competitive performance and provides a robust framework for building resource-light DST models.
A Named Entity Recognition Shootout for German
Authors: Martin Riedl, Sebastian Pad{\'o}
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Entity Linking, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Question Answering, Representation Learning, Small Data Image Classification, Transfer Learning

We ask how to practically build a model for German named entity recognition (NER) that performs at the state of the art for both contemporary and historical texts, i.e., a big-data and a small-data scenario. The two best-performing model families are pitted against each other (linear-chain CRFs and BiLSTM) to observe the trade-off between expressiveness and data requirements. BiLSTM outperforms the CRF when large datasets are available and performs inferior for the smallest dataset. BiLSTMs profit substantially from transfer learning, which enables them to be trained on multiple corpora, resulting in a new state-of-the-art model for German NER on two contemporary German corpora (CoNLL 2003 and GermEval 2014) and two historic corpora.
A dataset for identifying actionable feedback in collaborative software development
Authors: Benjamin S. Meyers, Nuthan Munaiah, Emily Prud{'}hommeaux, Andrew Meneely, Josephine Wolff, Cecilia Ovesdotter Alm, Pradeep Murukannaiah
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7

Software developers and testers have long struggled with how to elicit proactive responses from their coworkers when reviewing code for security vulnerabilities and errors. For a code review to be successful, it must not only identify potential problems but also elicit an active response from the colleague responsible for modifying the code. To understand the factors that contribute to this outcome, we analyze a novel dataset of more than one million code reviews for the Google Chromium project, from which we extract linguistic features of feedback that elicited responsive actions from coworkers. Using a manually-labeled subset of reviewer comments, we trained a highly accurate classifier to identify acted-upon comments (AUC = 0.85). Our results demonstrate the utility of our dataset, the feasibility of using NLP for this new task, and the potential of NLP to improve our understanding of how communications between colleagues can be authored to elicit positive, proactive responses.
SNAG: Spoken Narratives and Gaze Dataset
Authors: Preethi Vaidyanathan, Emily T. Prud{'}hommeaux, Jeff B. Pelz, Cecilia O. Alm
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7

Humans rely on multiple sensory modalities when examining and reasoning over images. In this paper, we describe a new multimodal dataset that consists of gaze measurements and spoken descriptions collected in parallel during an image inspection task. The task was performed by multiple participants on 100 general-domain images showing everyday objects and activities. We demonstrate the usefulness of the dataset by applying an existing visual-linguistic data fusion framework in order to label important image regions with appropriate linguistic labels.
Construction of a Chinese Corpus for the Analysis of the Emotionality of Metaphorical Expressions
Authors: Dongyu Zhang, Hongfei Lin, Liang Yang, Shaowu Zhang, Bo Xu
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Emotion Recognition

Metaphors are frequently used to convey emotions. However, there is little research on the construction of metaphor corpora annotated with emotion for the analysis of emotionality of metaphorical expressions. Furthermore, most studies focus on English, and few in other languages, particularly Sino-Tibetan languages such as Chinese, for emotion analysis from metaphorical texts, although there are likely to be many differences in emotional expressions of metaphorical usages across different languages. We therefore construct a significant new corpus on metaphor, with 5,605 manually annotated sentences in Chinese. We present an annotation scheme that contains annotations of linguistic metaphors, emotional categories (joy, anger, sadness, fear, love, disgust and surprise), and intensity. The annotation agreement analyses for multiple annotators are described. We also use the corpus to explore and analyze the emotionality of metaphors. To the best of our knowledge, this is the first relatively large metaphor corpus with an annotation of emotions in Chinese.
Improved Evaluation Framework for Complex Plagiarism Detection
Authors: Anton Belyy, Marina Dubova, Dmitry Nekrasov
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7

Plagiarism is a major issue in science and education. Complex plagiarism, such as plagiarism of ideas, is hard to detect, and therefore it is especially important to track improvement of methods correctly. In this paper, we study the performance of plagdet, the main measure for plagiarim detection, on manually paraphrased datasets (such as PAN Summary). We reveal its fallibility under certain conditions and propose an evaluation framework with normalization of inner terms, which is resilient to the dataset imbalance. We conclude with the experimental justification of the proposed measure. The implementation of the new framework is made publicly available as a Github repository.
A Language Model based Evaluator for Sentence Compression
Authors: Yang Zhao, Zhiyuan Luo, Akiko Aizawa
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Language Modeling, Language Modelling, model, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sentence, Sentence Compression

We herein present a language-model-based evaluator for deletion-based sentence compression and view this task as a series of deletion-and-evaluation operations using the evaluator. More specifically, the evaluator is a syntactic neural language model that is first built by learning the syntactic and structural collocation among words. Subsequently, a series of trial-and-error deletion operations are conducted on the source sentences via a reinforcement learning framework to obtain the best target compression. An empirical study shows that the proposed model can effectively generate more readable compression, comparable or superior to several strong baselines. Furthermore, we introduce a 200-sentence test set for a large-scale dataset, setting a new baseline for the future research.
Diachronic degradation of language models: Insights from social media
Authors: Kokil Jaidka, Niyati Chhaya, Lyle Ungar
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Word Embeddings

Natural languages change over time because they evolve to the needs of their users and the socio-technological environment. This study investigates the diachronic accuracy of pre-trained language models for downstream tasks in machine learning and user profiling. It asks the question: given that the social media platform and its users remain the same, how is language changing over time? How can these differences be used to track the changes in the affect around a particular topic? To our knowledge, this is the first study to show that it is possible to measure diachronic semantic drifts within social media and within the span of a few years.
Task-oriented Dialogue System for Automatic Diagnosis
Authors: Zhongyu Wei, Qianlong Liu, Baolin Peng, Huaixiao Tou, Ting Chen, Xuanjing Huang, Kam-Fai Wong, Xiangying Dai
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7

In this paper, we make a move to build a dialogue system for automatic diagnosis. We first build a dataset collected from an online medical forum by extracting symptoms from both patients{'} self-reports and conversational data between patients and doctors. Then we propose a task-oriented dialogue system framework to make diagnosis for patients automatically, which can converse with patients to collect additional symptoms beyond their self-reports. Experimental results on our dataset show that additional symptoms extracted from conversation can greatly improve the accuracy for disease identification and our dialogue system is able to collect these symptoms automatically and make a better diagnosis.
A Multi-task Approach to Learning Multilingual Representations
Authors: Karan Singla, Dogan Can, Shrikanth Narayanan
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Cross-Lingual Document Classification, Document Classification, General Classification, Sentence, Sentence Embeddings, Sentence Similarity, Word Embeddings

We present a novel multi-task modeling approach to learning multilingual distributed representations of text. Our system learns word and sentence embeddings jointly by training a multilingual skip-gram model together with a cross-lingual sentence similarity model. Our architecture can transparently use both monolingual and sentence aligned bilingual corpora to learn multilingual embeddings, thus covering a vocabulary significantly larger than the vocabulary of the bilingual corpora alone. Our model shows competitive performance in a standard cross-lingual document classification task. We also show the effectiveness of our method in a limited resource scenario.
A Study of the Importance of External Knowledge in the Named Entity Recognition Task
Authors: Dominic Seyler, Tatiana Dembelova, Luciano del Corro, Johannes Hoffart, Gerhard Weikum
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Question Answering

In this work, we discuss the importance of external knowledge for performing Named Entity Recognition (NER). We present a novel modular framework that divides the knowledge into four categories according to the depth of knowledge they convey. Each category consists of a set of features automatically generated from different information sources, such as a knowledge-base, a list of names, or document-specific semantic annotations. Further, we show the effects on performance when incrementally adding deeper knowledge and discuss effectiveness/efficiency trade-offs.
Improving Topic Quality by Promoting Named Entities in Topic Modeling
Authors: Katsiaryna Krasnashchok, Salim Jouili
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Diversity, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Specificity

News related content has been extensively studied in both topic modeling research and named entity recognition. However, expressive power of named entities and their potential for improving the quality of discovered topics has not received much attention. In this paper we use named entities as domain-specific terms for news-centric content and present a new weighting model for Latent Dirichlet Allocation. Our experimental results indicate that involving more named entities in topic descriptors positively influences the overall quality of topics, improving their interpretability, specificity and diversity.
CNN for Text-Based Multiple Choice Question Answering
Authors: Akshay Chaturvedi, P, Onkar it, Utpal Garain
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Multiple-choice, Question Answering, Reading Comprehension, Sentiment Analysis

The task of Question Answering is at the very core of machine comprehension. In this paper, we propose a Convolutional Neural Network (CNN) model for text-based multiple choice question answering where questions are based on a particular article. Given an article and a multiple choice question, our model assigns a score to each question-option tuple and chooses the final option accordingly. We test our model on Textbook Question Answering (TQA) and SciQ dataset. Our model outperforms several LSTM-based baseline models on the two datasets.
A Simple and Effective Approach to Coverage-Aware Neural Machine Translation
Authors: Yanyang Li, Tong Xiao, Yinqiao Li, Qiang Wang, Changming Xu, Jingbo Zhu
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Machine Translation, NMT, Reranking, Translation

We offer a simple and effective method to seek a better balance between model confidence and length preference for Neural Machine Translation (NMT). Unlike the popular length normalization and coverage models, our model does not require training nor reranking the limited n-best outputs. Moreover, it is robust to large beam sizes, which is not well studied in previous work. On the Chinese-English and English-German translation tasks, our approach yields +0.4 1.5 BLEU improvements over the state-of-the-art baselines.
Improving Beam Search by Removing Monotonic Constraint for Neural Machine Translation
Authors: Raphael Shu, Hideki Nakayama
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Language Modeling, Language Modelling, Machine Translation, Translation

To achieve high translation performance, neural machine translation models usually rely on the beam search algorithm for decoding sentences. The beam search finds good candidate translations by considering multiple hypotheses of translations simultaneously. However, as the algorithm produces hypotheses in a monotonic left-to-right order, a hypothesis can not be revisited once it is discarded. We found such monotonicity forces the algorithm to sacrifice some good decoding paths. To mitigate this problem, we relax the monotonic constraint of the beam search by maintaining all found hypotheses in a single priority queue and using a universal score function for hypothesis selection. The proposed algorithm allows discarded hypotheses to be recovered in a later step. Despite its simplicity, we show that the proposed decoding algorithm enhances the quality of selected hypotheses and improve the translations even for high-performance models in English-Japanese translation task.
Leveraging distributed representations and lexico-syntactic fixedness for token-level prediction of the idiomaticity of English verb-noun combinations
Authors: Milton King, Paul Cook
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Machine Translation, Sentence Embeddings, Word Embeddings

Verb-noun combinations (VNCs) - e.g., blow the whistle, hit the roof, and see stars - are a common type of English idiom that are ambiguous with literal usages. In this paper we propose and evaluate models for classifying VNC usages as idiomatic or literal, based on a variety of approaches to forming distributed representations. Our results show that a model based on averaging word embeddings performs on par with, or better than, a previously-proposed approach based on skip-thoughts. Idiomatic usages of VNCs are known to exhibit lexico-syntactic fixedness. We further incorporate this information into our models, demonstrating that this rich linguistic knowledge is complementary to the information carried by distributed representations.
Using pseudo-senses for improving the extraction of synonyms from word embeddings
Authors: Olivier Ferret
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Dimensionality Reduction, Semantic Similarity, Semantic Textual Similarity, Word Embeddings, Word Sense Disambiguation

The methods proposed recently for specializing word embeddings according to a particular perspective generally rely on external knowledge. In this article, we propose Pseudofit, a new method for specializing word embeddings according to semantic similarity without any external knowledge. Pseudofit exploits the notion of pseudo-sense for building several representations for each word and uses these representations for making the initial embeddings more generic. We illustrate the interest of Pseudofit for acquiring synonyms and study several variants of Pseudofit according to this perspective.
Neural Hidden Markov Model for Machine Translation
Authors: Weiyue Wang, Derui Zhu, Tamer Alkhouli, Zixuan Gan, Hermann Ney
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Decoder, Machine Translation, model, NMT, Translation

Attention-based neural machine translation (NMT) models selectively focus on specific source positions to produce a translation, which brings significant improvements over pure encoder-decoder sequence-to-sequence models. This work investigates NMT while replacing the attention component. We study a neural hidden Markov model (HMM) consisting of neural network-based alignment and lexicon models, which are trained jointly using the forward-backward algorithm. We show that the attention component can be effectively replaced by the neural network alignment model and the neural HMM approach is able to provide comparable performance with the state-of-the-art attention-based models on the WMT 2017 German↔English and Chinese→English translation tasks.
Orthographic Features for Bilingual Lexicon Induction
Authors: Parker Riley, Daniel Gildea
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Bilingual Lexicon Induction, Machine Translation, Multilingual Word Embeddings, Unsupervised Machine Translation, Word Alignment, Word Embeddings

Recent embedding-based methods in bilingual lexicon induction show good results, but do not take advantage of orthographic features, such as edit distance, which can be helpful for pairs of related languages. This work extends embedding-based methods to incorporate these features, resulting in significant accuracy gains for related languages.
Judicious Selection of Training Data in Assisting Language for Multilingual Neural NER
Authors: Rudra Murthy, Anoop Kunchukuttan, Pushpak Bhattacharyya
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Domain Adaptation, Machine Translation, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, TAG

Multilingual learning for Neural Named Entity Recognition (NNER) involves jointly training a neural network for multiple languages. Typically, the goal is improving the NER performance of one of the languages (the primary language) using the other assisting languages. We show that the divergence in the tag distributions of the common named entities between the primary and assisting languages can reduce the effectiveness of multilingual learning. To alleviate this problem, we propose a metric based on symmetric KL divergence to filter out the highly divergent training instances in the assisting language. We empirically show that our data selection strategy improves NER performance in many languages, including those with very limited training data.
Document Embedding Enhanced Event Detection with Hierarchical and Supervised Attention
Authors: Yue Zhao, Xiaolong Jin, Yuanzhuo Wang, Xue-Qi Cheng
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Document Embedding, Event Detection, Sentence

Document-level information is very important for event detection even at sentence level. In this paper, we propose a novel Document Embedding Enhanced Bi-RNN model, called DEEB-RNN, to detect events in sentences. This model first learns event detection oriented embeddings of documents through a hierarchical and supervised attention based RNN, which pays word-level attention to event triggers and sentence-level attention to those sentences containing events. It then uses the learned document embedding to enhance another bidirectional RNN model to identify event triggers and their types in sentences. Through experiments on the ACE-2005 dataset, we demonstrate the effectiveness and merits of the proposed DEEB-RNN model via comparison with state-of-the-art methods.
Improving Slot Filling in Spoken Language Understanding with Joint Pointer and Attention
Authors: Lin Zhao, Zhe Feng
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Sentence, slot-filling, Slot Filling, Speech Recognition, Spoken Language Understanding

We present a generative neural network model for slot filling based on a sequence-to-sequence (Seq2Seq) model together with a pointer network, in the situation where only sentence-level slot annotations are available in the spoken dialogue data. This model predicts slot values by jointly learning to copy a word which may be out-of-vocabulary (OOV) from an input utterance through a pointer network, or generate a word within the vocabulary through an attentional Seq2Seq model. Experimental results show the effectiveness of our slot filling model, especially at addressing the OOV problem. Additionally, we integrate the proposed model into a spoken language understanding system and achieve the state-of-the-art performance on the benchmark data.
Modeling discourse cohesion for discourse parsing via memory network
Authors: Yanyan Jia, Yuan Ye, Yansong Feng, Yuxuan Lai, Rui Yan, Dongyan Zhao
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Discourse Parsing, Question Answering, Sentiment Analysis

Identifying long-span dependencies between discourse units is crucial to improve discourse parsing performance. Most existing approaches design sophisticated features or exploit various off-the-shelf tools, but achieve little success. In this paper, we propose a new transition-based discourse parser that makes use of memory networks to take discourse cohesion into account. The automatically captured discourse cohesion benefits discourse parsing, especially for long span scenarios. Experiments on the RST discourse treebank show that our method outperforms traditional featured based methods, and the memory based discourse cohesion can improve the overall parsing performance significantly.
Predicting accuracy on large datasets from smaller pilot data
Authors: Mark Johnson, Peter Anderson, Mark Dras, Mark Steedman
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Document Classification

Because obtaining training data is often the most difficult part of an NLP or ML project, we develop methods for predicting how much data is required to achieve a desired test accuracy by extrapolating results from models trained on a small pilot training dataset. We model how accuracy varies as a function of training size on subsets of the pilot data, and use that model to predict how much training data would be required to achieve the desired accuracy. We introduce a new performance extrapolation task to evaluate how well different extrapolations predict accuracy on larger training sets. We show that details of hyperparameter optimisation and the extrapolation models can have dramatic effects in a document classification task. We believe this is an important first step in developing methods for estimating the resources required to meet specific engineering performance targets.