pwc-archive/papers-with-abstracts huggingface dataset:
Measuring the Effect of Conversational Aspects on Machine Translation Quality
Authors: Marlies van der Wees, Arianna Bisazza, Christof Monz
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Machine Translation, Translation

Research in statistical machine translation (SMT) is largely driven by formal translation tasks, while translating informal text is much more challenging. In this paper we focus on SMT for the informal genre of dialogues, which has rarely been addressed to date. Concretely, we investigate the effect of dialogue acts, speakers, gender, and text register on SMT quality when translating fictional dialogues. We first create and release a corpus of multilingual movie dialogues annotated with these four dialogue-specific aspects. When measuring translation performance for each of these variables, we find that BLEU fluctuations between their categories are often significantly larger than randomly expected. Following this finding, we hypothesize and show that SMT of fictional dialogues benefits from adaptation towards dialogue acts and registers. Finally, we find that male speakers are harder to translate and use more vulgar language than female speakers, and that vulgarity is often not preserved during translation.
Enriching Phrase Tables for Statistical Machine Translation Using Mixed Embeddings
Authors: Peyman Passban, Qun Liu, Andy Way
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Decoder, Document Classification, Machine Translation, Sentence, Translation, Word Embeddings

The phrase table is considered to be the main bilingual resource for the phrase-based statistical machine translation (PBSMT) model. During translation, a source sentence is decomposed into several phrases. The best match of each source phrase is selected among several target-side counterparts within the phrase table, and processed by the decoder to generate a sentence-level translation. The best match is chosen according to several factors, including a set of bilingual features. PBSMT engines by default provide four probability scores in phrase tables which are considered as the main set of bilingual features. Our goal is to enrich that set of features, as a better feature set should yield better translations. We propose new scores generated by a Convolutional Neural Network (CNN) which indicate the semantic relatedness of phrase pairs. We evaluate our model in different experimental settings with different language pairs. We observe significant improvements when the proposed features are incorporated into the PBSMT pipeline.
Anecdote Recognition and Recommendation
Authors: Wei Song, Ruiji Fu, Lizhen Liu, Hanshi Wang, Ting Liu
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12

We introduce a novel task Anecdote Recognition and Recommendation. An anecdote is a story with a point revealing account of an individual person. Recommending proper anecdotes can be used as evidence to support argumentative writing or as a clue for further reading. We represent an anecdote as a structured tuple {---} {\textless} person, story, implication {\textgreater}. Anecdote recognition runs on archived argumentative essays. We extract narratives containing events of a person as the anecdote story. More importantly, we uncover the anecdote implication, which reveals the meaning and topic of an anecdote. Our approach depends on discourse role identification. Discourse roles such as thesis, main ideas and support help us locate stories and their implications in essays. The experiments show that informative and interpretable anecdotes can be recognized. These anecdotes are used for anecdote recommendation. The anecdote recommender can recommend proper anecdotes in response to given topics. The anecdote implication contributes most for bridging user interested topics and relevant anecdotes.
Training Data Enrichment for Infrequent Discourse Relations
Authors: Kailang Jiang, Giuseppe Carenini, Raymond Ng
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Discourse Parsing, Relation, Sentiment Analysis

Discourse parsing is a popular technique widely used in text understanding, sentiment analysis and other NLP tasks. However, for most discourse parsers, the performance varies significantly across different discourse relations. In this paper, we first validate the underfitting hypothesis, i.e., the less frequent a relation is in the training data, the poorer the performance on that relation. We then explore how to increase the number of positive training instances, without resorting to manually creating additional labeled data. We propose a training data enrichment framework that relies on co-training of two different discourse parsers on unlabeled documents. Importantly, we show that co-training alone is not sufficient. The framework requires a filtering step to ensure that only {``}good quality{''} unlabeled documents can be used for enrichment and re-training. We propose and evaluate two ways to perform the filtering. The first is to use an agreement score between the two parsers. The second is to use only the confidence score of the faster parser. Our empirical results show that agreement score can help to boost the performance on infrequent relations, and that the confidence score is a viable approximation of the agreement score for infrequent relations.
Inferring Discourse Relations from PDTB-style Discourse Labels for Argumentative Revision Classification
Authors: Fan Zhang, Diane Litman, Katherine Forbes Riley
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Classification, General Classification

Penn Discourse Treebank (PDTB)-style annotation focuses on labeling local discourse relations between text spans and typically ignores larger discourse contexts. In this paper we propose two approaches to infer discourse relations in a paragraph-level context from annotated PDTB labels. We investigate the utility of inferring such discourse information using the task of revision classification. Experimental results demonstrate that the inferred information can significantly improve classification performance compared to baselines, not only when PDTB annotation comes from humans but also from automatic parsers.
Capturing Pragmatic Knowledge in Article Usage Prediction using LSTMs
Authors: Jad Kabbara, Yulan Feng, Jackie Chi Kit Cheung
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Grammatical Error Detection, Machine Translation, Prediction, Word Embeddings

We examine the potential of recurrent neural networks for handling pragmatic inferences involving complex contextual cues for the task of article usage prediction. We train and compare several variants of Long Short-Term Memory (LSTM) networks with an attention mechanism. Our model outperforms a previous state-of-the-art system, achieving up to 96.63{\%} accuracy on the WSJ/PTB corpus. In addition, we perform a series of analyses to understand the impact of various model choices. We find that the gain in performance can be attributed to the ability of LSTMs to pick up on contextual cues, both local and further away in distance, and that the model is able to solve cases involving reasoning about coreference and synonymy. We also show how the attention mechanism contributes to the interpretability of the model{'}s effectiveness.
Aspect Based Sentiment Analysis using Sentiment Flow with Local and Non-local Neighbor Information
Authors: Shubham Pateria
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Prediction, Sentiment Analysis, Structured Prediction

Aspect-level analysis of sentiments contained in a review text is important to reveal a detailed picture of consumer opinions. While a plethora of methods have been traditionally employed for this task, majority focus has been on analyzing only aspect-centered local information. However, incorporating context information from non-local aspect neighbors may capture richer structure in review text and enhance prediction. This may especially be helpful to resolve ambiguous predictions. The context around an aspect can be incorporated using semantic relations within text and inter-label dependencies in the output. On the output side, this becomes a structured prediction task. However, non-local label correlations are computationally heavy and intractable to infer for structured prediction models like Conditional Random Fields (CRF). Moreover, some prior intuition is required to incorporate non-local context. Thus, inspired by previous research on multi-stage prediction, we propose a two-level model for aspect-based analysis. The proposed model uses predicted probability estimates from first level to incorporate neighbor information in the second level. The model is evaluated on data taken from SemEval Workshops and Bing Liu{'}s review collection. It shows comparatively better performance against few existing methods. Overall, we get prediction accuracy in a range of 83-88{\%} and almost 3-4 point increment against baseline (first level only) scores.
Two-View Label Propagation to Semi-supervised Reader Emotion Classification
Authors: Shoushan Li, Jian Xu, Dong Zhang, Guodong Zhou
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Classification, Emotion Classification, General Classification, Vocal Bursts Valence Prediction

In the literature, various supervised learning approaches have been adopted to address the task of reader emotion classification. However, the classification performance greatly suffers when the size of the labeled data is limited. In this paper, we propose a two-view label propagation approach to semi-supervised reader emotion classification by exploiting two views, namely source text and response text in a label propagation algorithm. Specifically, our approach depends on two word-document bipartite graphs to model the relationship among the samples in the two views respectively. Besides, the two bipartite graphs are integrated by linking each source text sample with its corresponding response text sample via a length-sensitive transition probability. In this way, our two-view label propagation approach to semi-supervised reader emotion classification largely alleviates the reliance on the strong sufficiency and independence assumptions of the two views, as required in co-training. Empirical evaluation demonstrates the effectiveness of our two-view label propagation approach to semi-supervised reader emotion classification.
A Joint Sentiment-Target-Stance Model for Stance Classification in Tweets
Authors: Javid Ebrahimi, Dejing Dou, Daniel Lowd
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Argument Mining, Classification, General Classification, Opinion Mining, Sentiment Analysis, Stance Classification, Stance Detection, Subjectivity Analysis

Classifying the stance expressed in online microblogging social media is an emerging problem in opinion mining. We propose a probabilistic approach to stance classification in tweets, which models stance, target of stance, and sentiment of tweet, jointly. Instead of simply conjoining the sentiment or target variables as extra variables to the feature space, we use a novel formulation to incorporate three-way interactions among sentiment-stance-input variables and three-way interactions among target-stance-input variables. The proposed specification intuitively aims to discriminate sentiment features from target features for stance classification. In addition, regularizing a single stance classifier, which handles all targets, acts as a soft weight-sharing among them. We demonstrate that discriminative training of this model achieves the state-of-the-art results in supervised stance classification, and its generative training obtains competitive results in the weakly supervised setting.
SenticNet 4: A Semantic Resource for Sentiment Analysis Based on Conceptual Primitives
Authors: Erik Cambria, Soujanya Poria, Rajiv Bajpai, Bjoern Schuller
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Clustering, Dimensionality Reduction, Sentiment Analysis

An important difference between traditional AI systems and human intelligence is the human ability to harness commonsense knowledge gleaned from a lifetime of learning and experience to make informed decisions. This allows humans to adapt easily to novel situations where AI fails catastrophically due to a lack of situation-specific rules and generalization capabilities. Commonsense knowledge also provides background information that enables humans to successfully operate in social situations where such knowledge is typically assumed. Since commonsense consists of information that humans take for granted, gathering it is an extremely difficult task. Previous versions of SenticNet were focused on collecting this kind of knowledge for sentiment analysis but they were heavily limited by their inability to generalize. SenticNet 4 overcomes such limitations by leveraging on conceptual primitives automatically generated by means of hierarchical clustering and dimensionality reduction.
Latent Topic Embedding
Authors: Di Jiang, Lei Shi, Rongzhong Lian, Hua Wu
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Sentence, Topic Models, Word Embeddings

Topic modeling and word embedding are two important techniques for deriving latent semantics from data. General-purpose topic models typically work in coarse granularity by capturing word co-occurrence at the document/sentence level. In contrast, word embedding models usually work in much finer granularity by modeling word co-occurrence within small sliding windows. With the aim of deriving latent semantics by considering word co-occurrence at different levels of granularity, we propose a novel model named \textit{Latent Topic Embedding} (LTE), which seamlessly integrates topic generation and embedding learning in one unified framework. We further propose an efficient Monte Carlo EM algorithm to estimate the parameters of interest. By retaining the individual advantages of topic modeling and word embedding, LTE results in better latent topics and word embedding. Extensive experiments verify the superiority of LTE over the state-of-the-arts.
Integrating Distributional and Lexical Information for Semantic Classification of Words using MRMF
Authors: Rosa Tsegaye Aga, Lucas Drumond, Christian Wartena, Lars Schmidt-Thieme
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: General Classification, Semantic Similarity, Semantic Textual Similarity

Semantic classification of words using distributional features is usually based on the semantic similarity of words. We show on two different datasets that a trained classifier using the distributional features directly gives better results. We use Support Vector Machines (SVM) and Multi-relational Matrix Factorization (MRMF) to train classifiers. Both give similar results. However, MRMF, that was not used for semantic classification with distributional features before, can easily be extended with more matrices containing more information from different sources on the same problem. We demonstrate the effectiveness of the novel approach by including information from WordNet. Thus we show, that MRMF provides an interesting approach for building semantic classifiers that (1) gives better results than unsupervised approaches based on vector similarity, (2) gives similar results as other supervised methods and (3) can naturally be extended with other sources of information in order to improve the results.
Monday mornings are my fave :) \#not Exploring the Automatic Recognition of Irony in English tweets
Authors: Cynthia Van Hee, Els Lefever, V{\'e}ronique Hoste
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Opinion Mining, Sentiment Analysis

Recognising and understanding irony is crucial for the improvement natural language processing tasks including sentiment analysis. In this study, we describe the construction of an English Twitter corpus and its annotation for irony based on a newly developed fine-grained annotation scheme. We also explore the feasibility of automatic irony recognition by exploiting a varied set of features including lexical, syntactic, sentiment and semantic (Word2Vec) information. Experiments on a held-out test set show that our irony classifier benefits from this combined information, yielding an F1-score of 67.66{\%}. When explicit hashtag information like {\#}irony is included in the data, the system even obtains an F1-score of 92.77{\%}. A qualitative analysis of the output reveals that recognising irony that results from a polarity clash appears to be (much) more feasible than recognising other forms of ironic utterances (e.g., descriptions of situational irony).
CNN- and LSTM-based Claim Classification in Online User Comments
Authors: Chinnappa Guggilla, Tristan Miller, Iryna Gurevych
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Classification, General Classification, Sarcasm Detection, Word Embeddings

When processing arguments in online user interactive discourse, it is often necessary to determine their bases of support. In this paper, we describe a supervised approach, based on deep neural networks, for classifying the claims made in online arguments. We conduct experiments using convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) on two claim data sets compiled from online user comments. Using different types of distributional word embeddings, but without incorporating any rich, expensive set of features, we achieve a significant improvement over the state of the art for one data set (which categorizes arguments as factual vs. emotional), and performance comparable to the state of the art on the other data set (which categorizes propositions according to their verifiability). Our approach has the advantages of using a generalized, simple, and effective methodology that works for claim categorization on different data sets and tasks.
Experiments in Idiom Recognition
Authors: Jing Peng, Anna Feldman
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12

Some expressions can be ambiguous between idiomatic and literal interpretations depending on the context they occur in, e.g., {`}sales hit the roof{'} vs. {`}hit the roof of the car{'}. We present a novel method of classifying whether a given instance is literal or idiomatic, focusing on verb-noun constructions. We report state-of-the-art results on this task using an approach based on the hypothesis that the distributions of the contexts of the idiomatic phrases will be different from the contexts of the literal usages. We measure contexts by using projections of the words into vector space. For comparison, we implement Fazly et al. (2009){'}s, Sporleder and Li (2009){'}s, and Li and Sporleder (2010b){'}s methods and apply them to our data. We provide experimental results validating the proposed techniques.
Learning Succinct Models: Pipelined Compression with L1-Regularization, Hashing, Elias-Fano Indices, and Quantization
Authors: Hajime Senuma, Akiko Aizawa
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Chunking, Quantization, regression

The recent proliferation of smart devices necessitates methods to learn small-sized models. This paper demonstrates that if there are $m$ features in total but only $n = o(\sqrt{m})$ features are required to distinguish examples, with $\Omega(\log m)$ training examples and reasonable settings, it is possible to obtain a good model in a \textit{succinct} representation using $n \log_2 \frac{m}{n} + o(m)$ bits, by using a pipeline of existing compression methods: L1-regularized logistic regression, feature hashing, Elias{--}Fano indices, and randomized quantization. An experiment shows that a noun phrase chunking task for which an existing library requires 27 megabytes can be compressed to less than 13 \textit{kilo}bytes without notable loss of accuracy.
Bad Company---Neighborhoods in Neural Embedding Spaces Considered Harmful
Authors: Johannes Hellrich, Udo Hahn
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Word Embeddings

We assess the reliability and accuracy of (neural) word embeddings for both modern and historical English and German. Our research provides deeper insights into the empirically justified choice of optimal training methods and parameters. The overall low reliability we observe, nevertheless, casts doubt on the suitability of word neighborhoods in embedding spaces as a basis for qualitative conclusions on synchronic and diachronic lexico-semantic matters, an issue currently high up in the agenda of Digital Humanities.
Is an Image Worth More than a Thousand Words? On the Fine-Grain Semantic Differences between Visual and Linguistic Representations
Authors: Guillem Collell, Marie-Francine Moens
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12

Human concept representations are often grounded with visual information, yet some aspects of meaning cannot be visually represented or are better described with language. Thus, vision and language provide complementary information that, properly combined, can potentially yield more complete concept representations. Recently, state-of-the-art distributional semantic models and convolutional neural networks have achieved great success in representing linguistic and visual knowledge respectively. In this paper, we compare both, visual and linguistic representations in their ability to capture different types of fine-grain semantic knowledge{---}or attributes{---}of concepts. Humans often describe objects using attributes, that is, properties such as shape, color or functionality, which often transcend the linguistic and visual modalities. In our setting, we evaluate how well attributes can be predicted by using the unimodal representations as inputs. We are interested in first, finding out whether attributes are generally better captured by either the vision or by the language modality; and second, if none of them is clearly superior (as we hypothesize), what type of attributes or semantic knowledge are better encoded from each modality. Ultimately, our study sheds light on the potential of combining visual and textual representations.
On the contribution of word embeddings to temporal relation classification
Authors: Paramita Mirza, Sara Tonelli
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Classification, General Classification, Implicit Relations, Relation, Relation Classification, Temporal Relation Classification, Word Embeddings

Temporal relation classification is a challenging task, especially when there are no explicit markers to characterise the relation between temporal entities. This occurs frequently in inter-sentential relations, whose entities are not connected via direct syntactic relations making classification even more difficult. In these cases, resorting to features that focus on the semantic content of the event words may be very beneficial for inferring implicit relations. Specifically, while morpho-syntactic and context features are considered sufficient for classifying event-timex pairs, we believe that exploiting distributional semantic information about event words can benefit supervised classification of other types of pairs. In this work, we assess the impact of using word embeddings as features for event words in classifying temporal relations of event-event pairs and event-DCT (document creation time) pairs.