pwc-archive/papers-with-abstracts huggingface dataset:
Point Precisely: Towards Ensuring the Precision of Data in Generated Texts Using Delayed Copy Mechanism
Authors: Liunian Li, Xiaojun Wan
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Data-to-Text Generation, Decoder, Descriptive, Slot Filling, Text Generation

The task of data-to-text generation aims to generate descriptive texts conditioned on a number of database records, and recent neural models have shown significant progress on this task. The attention based encoder-decoder models with copy mechanism have achieved state-of-the-art results on a few data-to-text datasets. However, such models still face the problem of putting incorrect data records in the generated texts, especially on some more challenging datasets like RotoWire. In this paper, we propose a two-stage approach with a delayed copy mechanism to improve the precision of data records in the generated texts. Our approach first adopts an encoder-decoder model to generate a template text with data slots to be filled and then leverages a proposed delayed copy mechanism to fill in the slots with proper data records. Our delayed copy mechanism can take into account all the information of the input data records and the full generated template text by using double attention, position-aware attention and a pairwise ranking loss. The two models in the two stages are trained separately. Evaluation results on the RotoWire dataset verify the efficacy of our proposed approach to generate better templates and copy data records more precisely.
Enhancing General Sentiment Lexicons for Domain-Specific Use
Authors: Tim Kreutz, Walter Daelemans
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Domain Adaptation, domain classification, General Classification, Sentiment Analysis, Word Embeddings

Lexicon based methods for sentiment analysis rely on high quality polarity lexicons. In recent years, automatic methods for inducing lexicons have increased the viability of lexicon based methods for polarity classification. SentProp is a framework for inducing domain-specific polarities from word embeddings. We elaborate on SentProp by evaluating its use for enhancing DuOMan, a general-purpose lexicon, for use in the political domain. By adding only top sentiment bearing words from the vocabulary and applying small polarity shifts in the general-purpose lexicon, we increase accuracy in an in-domain classification task. The enhanced lexicon performs worse than the original lexicon in an out-domain task, showing that the words we added and the polarity shifts we applied are domain-specific and do not translate well to an out-domain setting.
An Operation Network for Abstractive Sentence Compression
Authors: Naitong Yu, Jie Zhang, Minlie Huang, Xiaoyan Zhu
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Sentence, Sentence Compression, Text Generation

Sentence compression condenses a sentence while preserving its most important contents. Delete-based models have the strong ability to delete undesired words, while generate-based models are able to reorder or rephrase the words, which are more coherent to human sentence compression. In this paper, we propose Operation Network, a neural network approach for abstractive sentence compression, which combines the advantages of both delete-based and generate-based sentence compression models. The central idea of Operation Network is to model the sentence compression process as an editing procedure. First, unnecessary words are deleted from the source sentence, then new words are either generated from a large vocabulary or copied directly from the source sentence. A compressed sentence can be obtained by a series of such edit operations (delete, copy and generate). Experiments show that Operation Network outperforms state-of-the-art baselines.
Enhanced Aspect Level Sentiment Classification with Auxiliary Memory
Authors: Peisong Zhu, Tieyun Qian
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Classification, Feature Engineering, General Classification, Machine Translation, Question Answering, Sentiment Analysis, Sentiment Classification, Text Classification, Text Summarization

In aspect level sentiment classification, there are two common tasks: to identify the sentiment of an aspect (category) or a term. As specific instances of aspects, terms explicitly occur in sentences. It is beneficial for models to focus on nearby context words. In contrast, as high level semantic concepts of terms, aspects usually have more generalizable representations. However, conventional methods cannot utilize the information of aspects and terms at the same time, because few datasets are annotated with both aspects and terms. In this paper, we propose a novel deep memory network with auxiliary memory to address this problem. In our model, a main memory is used to capture the important context words for sentiment classification. In addition, we build an auxiliary memory to implicitly convert aspects and terms to each other, and feed both of them to the main memory. With the interaction between two memories, the features of aspects and terms can be learnt simultaneously. We compare our model with the state-of-the-art methods on four datasets from different domains. The experimental results demonstrate the effectiveness of our model.
Author Profiling for Abuse Detection
Authors: Pushkar Mishra, Marco del Tredici, Helen Yannakoudakis, Ekaterina Shutova
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: 16k, Abuse Detection, Author Profiling

The rapid growth of social media in recent years has fed into some highly undesirable phenomena such as proliferation of hateful and offensive language on the Internet. Previous research suggests that such abusive content tends to come from users who share a set of common stereotypes and form communities around them. The current state-of-the-art approaches to abuse detection are oblivious to user and community information and rely entirely on textual (i.e., lexical and semantic) cues. In this paper, we propose a novel approach to this problem that incorporates community-based profiling features of Twitter users. Experimenting with a dataset of 16k tweets, we show that our methods significantly outperform the current state of the art in abuse detection. Further, we conduct a qualitative analysis of model characteristics. We release our code, pre-trained models and all the resources used in the public domain.
Automated Scoring: Beyond Natural Language Processing
Authors: Nitin Madnani, Aoife Cahill
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: BIG-bench Machine Learning, Position, valid

In this position paper, we argue that building operational automated scoring systems is a task that has disciplinary complexity above and beyond standard competitive shared tasks which usually involve applying the latest machine learning techniques to publicly available data in order to obtain the best accuracy. Automated scoring systems warrant significant cross-discipline collaboration of which natural language processing and machine learning are just two of many important components. Such systems have multiple stakeholders with different but valid perspectives that can often times be at odds with each other. Our position is that it is essential for us as NLP researchers to understand and incorporate these perspectives in our research and work towards a mutually satisfactory solution in order to build automated scoring systems that are accurate, fair, unbiased, and useful.
Aspect and Sentiment Aware Abstractive Review Summarization
Authors: Min Yang, Qiang Qu, Ying Shen, Qiao Liu, Wei Zhao, Jia Zhu
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Abstractive Text Summarization, Aspect Extraction, Decoder, Machine Translation, Sentiment Analysis, Text Categorization, Text Summarization

Review text has been widely studied in traditional tasks such as sentiment analysis and aspect extraction. However, to date, no work is towards the abstractive review summarization that is essential for business organizations and individual consumers to make informed decisions. This work takes the lead to study the aspect/sentiment-aware abstractive review summarization by exploring multi-factor attentions. Specifically, we propose an interactive attention mechanism to interactively learns the representations of context words, sentiment words and aspect words within the reviews, acted as an encoder. The learned sentiment and aspect representations are incorporated into the decoder to generate aspect/sentiment-aware review summaries via an attention fusion network. In addition, the abstractive summarizer is jointly trained with the text categorization task, which helps learn a category-specific text encoder, locating salient aspect information and exploring the variations of style and wording of content with respect to different text categories. The experimental results on a real-life dataset demonstrate that our model achieves impressive results compared to other strong competitors.
Effective Attention Modeling for Aspect-Level Sentiment Classification
Authors: Ruidan He, Wee Sun Lee, Hwee Tou Ng, Daniel Dahlmeier
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Aspect-Based Sentiment Analysis (ABSA), Classification, General Classification, Sentence, Sentiment Analysis, Sentiment Classification

Aspect-level sentiment classification aims to determine the sentiment polarity of a review sentence towards an opinion target. A sentence could contain multiple sentiment-target pairs; thus the main challenge of this task is to separate different opinion contexts for different targets. To this end, attention mechanism has played an important role in previous state-of-the-art neural models. The mechanism is able to capture the importance of each context word towards a target by modeling their semantic associations. We build upon this line of research and propose two novel approaches for improving the effectiveness of attention. First, we propose a method for target representation that better captures the semantic meaning of the opinion target. Second, we introduce an attention model that incorporates syntactic information into the attention mechanism. We experiment on attention-based LSTM (Long Short-Term Memory) models using the datasets from SemEval 2014, 2015, and 2016. The experimental results show that the conventional attention-based LSTM can be substantially improved by incorporating the two approaches.
Multilevel Heuristics for Rationale-Based Entity Relation Classification in Sentences
Authors: Shiou Tian Hsu, M Chaudhary, ar, Nagiza Samatova
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Classification, General Classification, Relation, Relation Classification, Sentence

Rationale-based models provide a unique way to provide justifiable results for relation classification models by identifying rationales (key words and phrases that a person can use to justify the relation in the sentence) during the process. However, existing generative networks used to extract rationales come with a trade-off between extracting diversified rationales and achieving good classification results. In this paper, we propose a multilevel heuristic approach to regulate rationale extraction to avoid extracting monotonous rationales without compromising classification performance. In our model, rationale selection is regularized by a semi-supervised process and features from different levels: word, syntax, sentence, and corpus. We evaluate our approach on the SemEval 2010 dataset that includes 19 relation classes and the quality of extracted rationales with our manually-labeled rationales. Experiments show a significant improvement in classification performance and a 20{\%} gain in rationale interpretability compared to state-of-the-art approaches.
Adversarial Multi-lingual Neural Relation Extraction
Authors: Xiaozhi Wang, Xu Han, Yankai Lin, Zhiyuan Liu, Maosong Sun
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Diversity, Question Answering, Relation, Relation Extraction, Sentence

Multi-lingual relation extraction aims to find unknown relational facts from text in various languages. Existing models cannot well capture the consistency and diversity of relation patterns in different languages. To address these issues, we propose an adversarial multi-lingual neural relation extraction (AMNRE) model, which builds both consistent and individual representations for each sentence to consider the consistency and diversity among languages. Further, we adopt an adversarial training strategy to ensure those consistent sentence representations could effectively extract the language-consistent relation patterns. The experimental results on real-world datasets demonstrate that our AMNRE model significantly outperforms the state-of-the-art models. The source code of this paper can be obtained from https://github.com/thunlp/AMNRE.
Neural Relation Classification with Text Descriptions
Authors: Feiliang Ren, Di Zhou, Zhihui Liu, Yongcheng Li, Rongsheng Zhao, Yongkang Liu, Xiaobo Liang
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Classification, General Classification, Relation, Relation Classification, Sentence

Relation classification is an important task in natural language processing fields. State-of-the-art methods usually concentrate on building deep neural networks based classification models on the training data in which the relations of the labeled entity pairs are given. However, these methods usually suffer from the data sparsity issue greatly. On the other hand, we notice that it is very easily to obtain some concise text descriptions for almost all of the entities in a relation classification task. The text descriptions can provide helpful supplementary information for relation classification. But they are ignored by most of existing methods. In this paper, we propose DesRC, a new neural relation classification method which integrates entities{'} text descriptions into deep neural networks models. We design a two-level attention mechanism to select the most useful information from the {``}intra-sentence{''} aspect and the {``}cross-sentence{''} aspect. Besides, the adversarial training method is also used to further improve the classification per-formance. Finally, we evaluate the proposed method on the SemEval 2010 dataset. Extensive experiments show that our method achieves much better experimental results than other state-of-the-art relation classification methods.
Abstractive Unsupervised Multi-Document Summarization using Paraphrastic Sentence Fusion
Authors: Mir Tafseer Nayeem, Tanvir Ahmed Fuad, Yllias Chali
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Abstractive Text Summarization, Document Summarization, Machine Translation, Multi-Document Summarization, Sentence, Sentence Compression, Sentence Fusion, Text Generation, Translation

In this work, we aim at developing an unsupervised abstractive summarization system in the multi-document setting. We design a paraphrastic sentence fusion model which jointly performs sentence fusion and paraphrasing using skip-gram word embedding model at the sentence level. Our model improves the information coverage and at the same time abstractiveness of the generated sentences. We conduct our experiments on the human-generated multi-sentence compression datasets and evaluate our system on several newly proposed Machine Translation (MT) evaluation metrics. Furthermore, we apply our sentence level model to implement an abstractive multi-document summarization system where documents usually contain a related set of sentences. We also propose an optimal solution for the classical summary length limit problem which was not addressed in the past research. For the document level summary, we conduct experiments on the datasets of two different domains (e.g., news article and user reviews) which are well suited for multi-document abstractive summarization. Our experiments demonstrate that the methods bring significant improvements over the state-of-the-art methods.
Dialogue-act-driven Conversation Model : An Experimental Study
Authors: Harshit Kumar, Arvind Agarwal, Sachindra Joshi
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Decoder, Dialogue Generation, model, Text Generation

The utility of additional semantic information for the task of next utterance selection in an automated dialogue system is the focus of study in this paper. In particular, we show that additional information available in the form of dialogue acts {--}when used along with context given in the form of dialogue history{--} improves the performance irrespective of the underlying model being generative or discriminative. In order to show the model agnostic behavior of dialogue acts, we experiment with several well-known models such as sequence-to-sequence encoder-decoder model, hierarchical encoder-decoder model, and Siamese-based models with and without hierarchy; and show that in all models, incorporating dialogue acts improves the performance by a significant margin. We, furthermore, propose a novel way of encoding dialogue act information, and use it along with hierarchical encoder to build a model that can use the sequential dialogue act information in a natural way. Our proposed model achieves an MRR of about 84.8{\%} for the task of next utterance selection on a newly introduced Daily Dialogue dataset, and outperform the baseline models. We also provide a detailed analysis of results including key insights that explain the improvement in MRR because of dialog act information.
Structured Dialogue Policy with Graph Neural Networks
Authors: Lu Chen, Bowen Tan, Sishan Long, Kai Yu
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Automatic Speech Recognition (ASR), Decision Making, Deep Reinforcement Learning, Dialogue Management, Management, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Speech Recognition, Spoken Language Understanding

Recently, deep reinforcement learning (DRL) has been used for dialogue policy optimization. However, many DRL-based policies are not sample-efficient. Most recent advances focus on improving DRL optimization algorithms to address this issue. Here, we take an alternative route of designing neural network structure that is better suited for DRL-based dialogue management. The proposed structured deep reinforcement learning is based on graph neural networks (GNN), which consists of some sub-networks, each one for a node on a directed graph. The graph is defined according to the domain ontology and each node can be considered as a sub-agent. During decision making, these sub-agents have internal message exchange between neighbors on the graph. We also propose an approach to jointly optimize the graph structure as well as the parameters of GNN. Experiments show that structured DRL significantly outperforms previous state-of-the-art approaches in almost all of the 18 tasks of the PyDial benchmark.
MEMD: A Diversity-Promoting Learning Framework for Short-Text Conversation
Authors: Meng Zou, Xihan Li, Haokun Liu, Zhi-Hong Deng
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Conversational Response Generation, Decoder, Diversity, Response Generation, Short-Text Conversation

Neural encoder-decoder models have been widely applied to conversational response generation, which is a research hot spot in recent years. However, conventional neural encoder-decoder models tend to generate commonplace responses like {``}I don{'}t know{''} regardless of what the input is. In this paper, we analyze this problem from a new perspective: latent vectors. Based on it, we propose an easy-to-extend learning framework named MEMD (Multi-Encoder to Multi-Decoder), in which an auxiliary encoder and an auxiliary decoder are introduced to provide necessary training guidance without resorting to extra data or complicating network{'}s inner structure. Experimental results demonstrate that our method effectively improve the quality of generated responses according to automatic metrics and human evaluations, yielding more diverse and smooth replies.
Fine-Grained Arabic Dialect Identification
Authors: Mohammad Salameh, Houda Bouamor, Nizar Habash
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Classification, Dialect Identification, General Classification, Machine Translation, Sentiment Analysis

Previous work on the problem of Arabic Dialect Identification typically targeted coarse-grained five dialect classes plus Standard Arabic (6-way classification). This paper presents the first results on a fine-grained dialect classification task covering 25 specific cities from across the Arab World, in addition to Standard Arabic {--} a very challenging task. We build several classification systems and explore a large space of features. Our results show that we can identify the exact city of a speaker at an accuracy of 67.9{\%} for sentences with an average length of 7 words (a 9{\%} relative error reduction over the state-of-the-art technique for Arabic dialect identification) and reach more than 90{\%} when we consider 16 words. We also report on additional insights from a data analysis of similarity and difference across Arabic dialects.
Who Feels What and Why? Annotation of a Literature Corpus with Semantic Roles of Emotions
Authors: Evgeny Kim, Roman Klinger
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Emotion Recognition

Most approaches to emotion analysis in fictional texts focus on detecting the emotion expressed in text. We argue that this is a simplification which leads to an overgeneralized interpretation of the results, as it does not take into account who experiences an emotion and why. Emotions play a crucial role in the interaction between characters and the events they are involved in. Until today, no specific corpora that capture such an interaction were available for literature. We aim at filling this gap and present a publicly available corpus based on Project Gutenberg, REMAN (Relational EMotion ANnotation), manually annotated for spans which correspond to emotion trigger phrases and entities/events in the roles of experiencers, targets, and causes of the emotion. We provide baseline results for the automatic prediction of these relational structures and show that emotion lexicons are not able to encompass the high variability of emotion expressions and demonstrate that statistical models benefit from joint modeling of emotions with its roles in all subtasks. The corpus that we provide enables future research on the recognition of emotions and associated entities in text. It supports qualitative literary studies and digital humanities. The corpus is available at http://www.ims.uni-stuttgart.de/data/reman .
Local String Transduction as Sequence Labeling
Authors: Joana Ribeiro, Shashi Narayan, Shay B. Cohen, Xavier Carreras
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Lemmatization, Machine Translation, Morphological Inflection, Named Entity Recognition (NER), Optical Character Recognition (OCR), Part-Of-Speech Tagging, Semantic Role Labeling, Spelling Correction

We show that the general problem of string transduction can be reduced to the problem of sequence labeling. While character deletion and insertions are allowed in string transduction, they do not exist in sequence labeling. We show how to overcome this difference. Our approach can be used with any sequence labeling algorithm and it works best for problems in which string transduction imposes a strong notion of locality (no long range dependencies). We experiment with spelling correction for social media, OCR correction, and morphological inflection, and we see that it behaves better than seq2seq models and yields state-of-the-art results in several cases.