pwc-archive/papers-with-abstracts huggingface dataset:
Attentive Language Models
Authors: Giancarlo Salton, Robert Ross, John Kelleher
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Image Captioning, Machine Translation, Speech Recognition

In this paper, we extend Recurrent Neural Network Language Models (RNN-LMs) with an attention mechanism. We show that an {``}attentive{''} RNN-LM (with 11M parameters) achieves a better perplexity than larger RNN-LMs (with 66M parameters) and achieves performance comparable to an ensemble of 10 similar sized RNN-LMs. We also show that an {``}attentive{''} RNN-LM needs less contextual information to achieve similar results to the state-of-the-art on the wikitext2 dataset.
Diachrony-aware Induction of Binary Latent Representations from Typological Features
Authors: Yugo Murawaki
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Missing Values

Although features of linguistic typology are a promising alternative to lexical evidence for tracing evolutionary history of languages, a large number of missing values in the dataset pose serious difficulties for statistical modeling. In this paper, we combine two existing approaches to the problem: (1) the synchronic approach that focuses on interdependencies between features and (2) the diachronic approach that exploits phylogenetically- and/or spatially-related languages. Specifically, we propose a Bayesian model that (1) represents each language as a sequence of binary latent parameters encoding inter-feature dependencies and (2) relates a language{'}s parameters to those of its phylogenetic and spatial neighbors. Experiments show that the proposed model recovers missing values more accurately than others and that induced representations retain phylogenetic and spatial signals observed for surface features.
Using Explicit Discourse Connectives in Translation for Implicit Discourse Relation Classification
Authors: Wei Shi, Frances Yung, Raphael Rubino, Vera Demberg
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: General Classification, Implicit Discourse Relation Classification, Machine Translation, Question Answering, Relation, Relation Classification, Translation

Implicit discourse relation recognition is an extremely challenging task due to the lack of indicative connectives. Various neural network architectures have been proposed for this task recently, but most of them suffer from the shortage of labeled data. In this paper, we address this problem by procuring additional training data from parallel corpora: When humans translate a text, they sometimes add connectives (a process known as \textit{explicitation}). We automatically back-translate it into an English connective and use it to infer a label with high confidence. We show that a training set several times larger than the original training set can be generated this way. With the extra labeled instances, we show that even a simple bidirectional Long Short-Term Memory Network can outperform the current state-of-the-art.
Implicit Syntactic Features for Target-dependent Sentiment Analysis
Authors: Yuze Gao, Yue Zhang, Tong Xiao
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Decoder, Representation Learning, Sentence, Sentiment Analysis

Targeted sentiment analysis investigates the sentiment polarities on given target mentions from input texts. Different from sentence level sentiment, it offers more fine-grained knowledge on each entity mention. While early work leveraged syntactic information, recent research has used neural representation learning to induce features automatically, thereby avoiding error propagation of syntactic parsers, which are particularly severe on social media texts. We study a method to leverage syntactic information without explicitly building the parser outputs, by training an encoder-decoder structure parser model on standard syntactic treebanks, and then leveraging its hidden encoder layers when analysing tweets. Such hidden vectors do not contain explicit syntactic outputs, yet encode rich syntactic features. We use them to augment the inputs to a baseline state-of-the-art targeted sentiment classifier, observing significant improvements on various benchmark datasets. We obtain the best accuracies on all test sets.
Graph Based Sentiment Aggregation using ConceptNet Ontology
Authors: Srikanth Tamilselvam, Seema Nagar, Abhijit Mishra, Kuntal Dey
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Sentiment Analysis

The sentiment aggregation problem accounts for analyzing the sentiment of a user towards various aspects/features of a product, and meaningfully assimilating the pragmatic significance of these features/aspects from an opinionated text. The current paper addresses the sentiment aggregation problem, by assigning weights to each aspect appearing in the user-generated content, that are proportionate to the strategic importance of the aspect in the pragmatic domain. The novelty of this paper is in computing the pragmatic significance (weight) of each aspect, using graph centrality measures (applied on domain specific ontology-graphs extracted from ConceptNet), and deeply ingraining these weights while aggregating the sentiments from opinionated text. We experiment over multiple real-life product review data. Our system consistently outperforms the state of the art - by as much as a F-score of 20.39{\%} in one case.
Sentence Modeling with Deep Neural Architecture using Lexicon and Character Attention Mechanism for Sentiment Classification
Authors: Huy Thanh Nguyen, Minh Le Nguyen
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Classification, General Classification, Sentence, Sentiment Analysis, Sentiment Classification, Word Embeddings

Tweet-level sentiment classification in Twitter social networking has many challenges: exploiting syntax, semantic, sentiment, and context in tweets. To address these problems, we propose a novel approach to sentiment analysis that uses lexicon features for building lexicon embeddings (LexW2Vs) and generates character attention vectors (CharAVs) by using a Deep Convolutional Neural Network (DeepCNN). Our approach integrates LexW2Vs and CharAVs with continuous word embeddings (ContinuousW2Vs) and dependency-based word embeddings (DependencyW2Vs) simultaneously in order to increase information for each word into a Bidirectional Contextual Gated Recurrent Neural Network (Bi-CGRNN). We evaluate our model on two Twitter sentiment classification datasets. Experimental results show that our model can improve the classification accuracy of sentence-level sentiment analysis in Twitter social networking.
Combining Lightly-Supervised Text Classification Models for Accurate Contextual Advertising
Authors: Yiping Jin, Dittaya Wanvarie, Phu Le
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Classification, General Classification, One-Class Classification, text-classification, Text Classification

In this paper we propose a lightly-supervised framework to rapidly build text classifiers for contextual advertising. Traditionally text classification techniques require labeled training documents for each predefined class. In the scenario of contextual advertising, advertisers often want to target to a specific class of webpages most relevant to their product or service, which may not be covered by a pre-trained classifier. Moreover, the advertisers are interested in whether a webpage is {``}relevant{''} or {``}irrelevant{''}. It is time-consuming to solicit the advertisers for reliable training signals for the negative class. Therefore, it is more suitable to model the problem as a one-class classification problem, in contrast to traditional classification problems where disjoint classes are defined a priori. We first apply two state-of-the-art lightly-supervised classification models, generalized expectation (GE) criteria (Druck et al., 2008) and multinomial naive Bayes (MNB) with priors (Settles, 2011) to one-class classification where the user only needs to provide a small list of labeled words for the target class. To combine the strengths of the two models, we fuse them together by using MNB to automatically enrich the constraints for GE training. We also explore ensemble method to combine classifiers. On a corpus of webpages from real-time bidding requests, the proposed model achieves the highest average F1 of 0.69 and closes more than half of the gap between previous state-of-the-art lightly-supervised models to a fully-supervised MaxEnt model.
Lightly-Supervised Modeling of Argument Persuasiveness
Authors: Isaac Persing, Vincent Ng
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Argument Mining, Persuasiveness

We propose the first lightly-supervised approach to scoring an argument{'}s persuasiveness. Key to our approach is the novel hypothesis that lightly-supervised persuasiveness scoring is possible by explicitly modeling the major errors that negatively impact persuasiveness. In an evaluation on a new annotated corpus of online debate arguments, our approach rivals its fully-supervised counterparts in performance by four scoring metrics when using only 10{\%} of the available training instances.
Chat Disentanglement: Identifying Semantic Reply Relationships with Random Forests and Recurrent Neural Networks
Authors: Shikib Mehri, Giuseppe Carenini
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Disentanglement

Thread disentanglement is a precursor to any high-level analysis of multiparticipant chats. Existing research approaches the problem by calculating the likelihood of two messages belonging in the same thread. Our approach leverages a newly annotated dataset to identify reply relationships. Furthermore, we explore the usage of an RNN, along with large quantities of unlabeled data, to learn semantic relationships between messages. Our proposed pipeline, which utilizes a reply classifier and an RNN to generate a set of disentangled threads, is novel and performs well against previous work.
Cascading Multiway Attentions for Document-level Sentiment Classification
Authors: Dehong Ma, Sujian Li, Xiaodong Zhang, Houfeng Wang, Xu sun
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Classification, General Classification, Product Recommendation, Sentence, Sentiment Analysis, Sentiment Classification

Document-level sentiment classification aims to assign the user reviews a sentiment polarity. Previous methods either just utilized the document content without consideration of user and product information, or did not comprehensively consider what roles the three kinds of information play in text modeling. In this paper, to reasonably use all the information, we present the idea that user, product and their combination can all influence the generation of attentions to words and sentences, when judging the sentiment of a document. With this idea, we propose a cascading multiway attention (CMA) model, where multiple ways of using user and product information are cascaded to influence the generation of attentions on the word and sentence layers. Then, sentences and documents are well modeled by multiple representation vectors, which provide rich information for sentiment classification. Experiments on IMDB and Yelp datasets demonstrate the effectiveness of our model.
An Ensemble Method with Sentiment Features and Clustering Support
Authors: Huy Tien Nguyen, Minh Le Nguyen
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Clustering, Deep Learning, General Classification, Information Retrieval, Sentence, Sentiment Analysis

Deep learning models have recently been applied successfully in natural language processing, especially sentiment analysis. Each deep learning model has a particular advantage, but it is difficult to combine these advantages into one model, especially in the area of sentiment analysis. In our approach, Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) were utilized to learn sentiment-specific features in a freezing scheme. This scenario provides a novel and efficient way for integrating advantages of deep learning models. In addition, we also grouped documents into clusters by their similarity and applied the prediction score of Naive Bayes SVM (NBSVM) method to boost the classification accuracy of each group. The experiments show that our method achieves the state-of-the-art performance on two well-known datasets: IMDB large movie reviews for document level and Pang {\&} Lee movie reviews for sentence level.
Leveraging Auxiliary Tasks for Document-Level Cross-Domain Sentiment Classification
Authors: Jianfei Yu, Jing Jiang
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Classification, Denoising, Domain Adaptation, General Classification, Opinion Mining, Sentence, Sentence Embeddings, Sentiment Analysis, Sentiment Classification

In this paper, we study domain adaptation with a state-of-the-art hierarchical neural network for document-level sentiment classification. We first design a new auxiliary task based on sentiment scores of domain-independent words. We then propose two neural network architectures to respectively induce document embeddings and sentence embeddings that work well for different domains. When these document and sentence embeddings are used for sentiment classification, we find that with both pseudo and external sentiment lexicons, our proposed methods can perform similarly to or better than several highly competitive domain adaptation methods on a benchmark dataset of product reviews.
Measuring Semantic Relations between Human Activities
Authors: Steven Wilson, Rada Mihalcea
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Semantic Textual Similarity

The things people do in their daily lives can provide valuable insights into their personality, values, and interests. Unstructured text data on social media platforms are rich in behavioral content, and automated systems can be deployed to learn about human activity on a broad scale if these systems are able to reason about the content of interest. In order to aid in the evaluation of such systems, we introduce a new phrase-level semantic textual similarity dataset comprised of human activity phrases, providing a testbed for automated systems that analyze relationships between phrasal descriptions of people{'}s actions. Our set of 1,000 pairs of activities is annotated by human judges across four relational dimensions including similarity, relatedness, motivational alignment, and perceived actor congruence. We evaluate a set of strong baselines for the task of generating scores that correlate highly with human ratings, and we introduce several new approaches to the phrase-level similarity task in the domain of human activities.
Learning Transferable Representation for Bilingual Relation Extraction via Convolutional Neural Networks
Authors: Bonan Min, Zhuolin Jiang, Marjorie Freedman, Ralph Weischedel
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Knowledge Base Population, Question Answering, Relation, Relation Classification, Relation Extraction, Word Embeddings

Typically, relation extraction models are trained to extract instances of a relation ontology using only training data from a single language. However, the concepts represented by the relation ontology (e.g. ResidesIn, EmployeeOf) are language independent. The numbers of annotated examples available for a given ontology vary between languages. For example, there are far fewer annotated examples in Spanish and Japanese than English and Chinese. Furthermore, using only language-specific training data results in the need to manually annotate equivalently large amounts of training for each new language a system encounters. We propose a deep neural network to learn transferable, discriminative bilingual representation. Experiments on the ACE 2005 multilingual training corpus demonstrate that the joint training process results in significant improvement in relation classification performance over the monolingual counterparts. The learnt representation is discriminative and transferable between languages. When using 10{\%} (25K English words, or 30K Chinese characters) of the training data, our approach results in doubling F1 compared to a monolingual baseline. We achieve comparable performance to the monolingual system trained with 250K English words (or 300K Chinese characters) With 50{\%} of training data.
Bilingual Word Embeddings for Bilingual Terminology Extraction from Specialized Comparable Corpora
Authors: Amir Hazem, Emmanuel Morin
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Word Embeddings

Bilingual lexicon extraction from comparable corpora is constrained by the small amount of available data when dealing with specialized domains. This aspect penalizes the performance of distributional-based approaches, which is closely related to the reliability of word{'}s cooccurrence counts extracted from comparable corpora. A solution to avoid this limitation is to associate external resources with the comparable corpus. Since bilingual word embeddings have recently shown efficient models for learning bilingual distributed representation of words, we explore different word embedding models and show how a general-domain comparable corpus can enrich a specialized comparable corpus via neural networks
A Bambara Tonalization System for Word Sense Disambiguation Using Differential Coding, Segmentation and Edit Operation Filtering
Authors: Luigi Yu-Cheng Liu, Damien Nouvel
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: BIG-bench Machine Learning, Word Sense Disambiguation

In many languages such as Bambara or Arabic, tone markers (diacritics) may be written but are actually often omitted. NLP applications are confronted to ambiguities and subsequent difficulties when processing texts. To circumvent this problem, tonalization may be used, as a word sense disambiguation task, relying on context to add diacritics that partially disambiguate words as well as senses. In this paper, we describe our implementation of a Bambara tonalizer that adds tone markers using machine learning (CRFs). To make our tool efficient, we used differential coding, word segmentation and edit operation filtering. We describe our approach that allows tractable machine learning and improves accuracy: our model may be learned within minutes on a 358K-word corpus and reaches 92.3{\%} accuracy.
Joint Learning of Dialog Act Segmentation and Recognition in Spoken Dialog Using Neural Networks
Authors: Tianyu Zhao, Tatsuya Kawahara
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Automatic Speech Recognition (ASR), Natural Language Understanding, Segmentation, Speech Recognition, Text Classification

Dialog act segmentation and recognition are basic natural language understanding tasks in spoken dialog systems. This paper investigates a unified architecture for these two tasks, which aims to improve the model{'}s performance on both of the tasks. Compared with past joint models, the proposed architecture can (1) incorporate contextual information in dialog act recognition, and (2) integrate models for tasks of different levels as a whole, i.e. dialog act segmentation on the word level and dialog act recognition on the segment level. Experimental results show that the joint training system outperforms the simple cascading system and the joint coding system on both dialog act segmentation and recognition tasks.
Predicting Users' Negative Feedbacks in Multi-Turn Human-Computer Dialogues
Authors: Xin Wang, Jianan Wang, Yuanchao Liu, Xiaolong Wang, Zhuoran Wang, Baoxun Wang
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Data Augmentation

User experience is essential for human-computer dialogue systems. However, it is impractical to ask users to provide explicit feedbacks when the agents{'} responses displease them. Therefore, in this paper, we explore to predict users{'} imminent dissatisfactions caused by intelligent agents by analysing the existing utterances in the dialogue sessions. To our knowledge, this is the first work focusing on this task. Several possible factors that trigger negative emotions are modelled. A relation sequence model (RSM) is proposed to encode the sequence of appropriateness of current response with respect to the earlier utterances. The experimental results show that the proposed structure is effective in modelling emotional risk (possibility of negative feedback) than existing conversation modelling approaches. Besides, strategies of obtaining distance supervision data for pre-training are also discussed in this work. Balanced sampling with respect to the last response in the distance supervision data are shown to be reliable for data augmentation.
Assessing the Verifiability of Attributions in News Text
Authors: Edward Newell, Ariane Schang, Drew Margolin, Derek Ruths
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Question Answering

When reporting the news, journalists rely on the statements of stakeholders, experts, and officials. The attribution of such a statement is verifiable if its fidelity to the source can be confirmed or denied. In this paper, we develop a new NLP task: determining the verifiability of an attribution based on linguistic cues. We operationalize the notion of verifiability as a score between 0 and 1 using human judgments in a comparison-based approach. Using crowdsourcing, we create a dataset of verifiability-scored attributions, and demonstrate a model that achieves an RMSE of 0.057 and Spearman{'}s rank correlation of 0.95 to human-generated scores. We discuss the application of this technique to the analysis of mass media.
Domain Adaptation from User-level Facebook Models to County-level Twitter Predictions
Authors: Daniel Rieman, Kokil Jaidka, H. Andrew Schwartz, Lyle Ungar
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Domain Adaptation

Several studies have demonstrated how language models of user attributes, such as personality, can be built by using the Facebook language of social media users in conjunction with their responses to psychology questionnaires. It is challenging to apply these models to make general predictions about attributes of communities, such as personality distributions across US counties, because it requires 1. the potentially inavailability of the original training data because of privacy and ethical regulations, 2. adapting Facebook language models to Twitter language without retraining the model, and 3. adapting from users to county-level collections of tweets. We propose a two-step algorithm, Target Side Domain Adaptation (TSDA) for such domain adaptation when no labeled Twitter/county data is available. TSDA corrects for the different word distributions between Facebook and Twitter and for the varying word distributions across counties by adjusting target side word frequencies; no changes to the trained model are made. In the case of predicting the Big Five county-level personality traits, TSDA outperforms a state-of-the-art domain adaptation method, gives county-level predictions that have fewer extreme outliers, higher year-to-year stability, and higher correlation with county-level outcomes.