pwc-archive/papers-with-abstracts huggingface dataset:
Building RDF Content for Data-to-Text Generation
Authors: Laura Perez-Beltrachini, Rania Sayed, Claire Gardent
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Data-to-Text Generation, Text Generation

In Natural Language Generation (NLG), one important limitation is the lack of common benchmarks on which to train, evaluate and compare data-to-text generators. In this paper, we make one step in that direction and introduce a method for automatically creating an arbitrary large repertoire of data units that could serve as input for generation. Using both automated metrics and a human evaluation, we show that the data units produced by our method are both diverse and coherent.
Parallel Sentence Compression
Authors: Julia Ive, Fran{\c{c}}ois Yvon
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Articles, Machine Translation, Semantic Role Labeling, Sentence, Sentence Compression, Text Simplification

Sentence compression is a way to perform text simplification and is usually handled in a monolingual setting. In this paper, we study ways to extend sentence compression in a bilingual context, where the goal is to obtain parallel compressions of parallel sentences. This can be beneficial for a series of multilingual natural language processing (NLP) tasks. We compare two ways to take bilingual information into account when compressing parallel sentences. Their efficiency is contrasted on a parallel corpus of News articles.
An Unsupervised Multi-Document Summarization Framework Based on Neural Document Model
Authors: Shulei Ma, Zhi-Hong Deng, Yunlun Yang
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Clustering, Document Summarization, Multi-Document Summarization, Sentence, Sentiment Analysis

In the age of information exploding, multi-document summarization is attracting particular attention for the ability to help people get the main ideas in a short time. Traditional extractive methods simply treat the document set as a group of sentences while ignoring the global semantics of the documents. Meanwhile, neural document model is effective on representing the semantic content of documents in low-dimensional vectors. In this paper, we propose a document-level reconstruction framework named DocRebuild, which reconstructs the documents with summary sentences through a neural document model and selects summary sentences to minimize the reconstruction error. We also apply two strategies, sentence filtering and beamsearch, to improve the performance of our method. Experimental results on the benchmark datasets DUC 2006 and DUC 2007 show that DocRebuild is effective and outperforms the state-of-the-art unsupervised algorithms.
From OpenCCG to AI Planning: Detecting Infeasible Edges in Sentence Generation
Authors: Maximilian Schwenger, {\'A}lvaro Torralba, Joerg Hoffmann, David M. Howcroft, Vera Demberg
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Sentence, Text Generation

The search space in grammar-based natural language generation tasks can get very large, which is particularly problematic when generating long utterances or paragraphs. Using surface realization with OpenCCG as an example, we show that we can effectively detect partial solutions (edges) which cannot ultimately be part of a complete sentence because of their syntactic category. Formulating the completion of an edge into a sentence as finding a solution path in a large state-transition system, we demonstrate a connection to AI Planning which is concerned with this kind of problem. We design a compilation from OpenCCG into AI Planning allowing the detection of infeasible edges via AI Planning dead-end detection methods (proving the absence of a solution to the compilation). Our experiments show that this can filter out large fractions of infeasible edges in, and thus benefit the performance of, complex realization processes.
The Next Step for Multi-Document Summarization: A Heterogeneous Multi-Genre Corpus Built with a Novel Construction Approach
Authors: Markus Zopf, Maxime Peyrard, Judith Eckle-Kohler
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Document Summarization, Multi-Document Summarization, Sentence

Research in multi-document summarization has focused on newswire corpora since the early beginnings. However, the newswire genre provides genre-specific features such as sentence position which are easy to exploit in summarization systems. Such easy to exploit genre-specific features are available in other genres as well. We therefore present the new hMDS corpus for multi-document summarization, which contains heterogeneous source documents from multiple text genres, as well as summaries with different lengths. For the construction of the corpus, we developed a novel construction approach which is suited to build large and heterogeneous summarization corpora with little effort. The method reverses the usual process of writing summaries for given source documents: it combines already available summaries with appropriate source documents. In a detailed analysis, we show that our new corpus is significantly different from the homogeneous corpora commonly used, and that it is heterogeneous along several dimensions. Our experimental evaluation using well-known state-of-the-art summarization systems shows that our corpus poses new challenges in the field of multi-document summarization. Last but not least, we make our corpus publicly available to the research community at the corpus web page \url{https://github.com/AIPHES/hMDS}.
On the Impact of Seed Words on Sentiment Polarity Lexicon Induction
Authors: Dame Jovanoski, Veno Pachovski, Preslav Nakov
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Sentiment Analysis, Text Classification

Sentiment polarity lexicons are key resources for sentiment analysis, and researchers have invested a lot of efforts in their manual creation. However, there has been a recent shift towards automatically extracted lexicons, which are orders of magnitude larger and perform much better. These lexicons are typically mined using bootstrapping, starting from very few seed words whose polarity is given, e.g., 50-60 words, and sometimes even just 5-6. Here we demonstrate that much higher-quality lexicons can be built by starting with hundreds of words and phrases as seeds, especially when they are in-domain. Thus, we combine (i) mid-sized high-quality manually crafted lexicons as seeds and (ii) bootstrapping, in order to build large-scale lexicons.
Evaluating Argumentative and Narrative Essays using Graphs
Authors: Swapna Somasundaran, Brian Riordan, Binod Gyawali, Su-Youn Yoon
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12

This work investigates whether the development of ideas in writing can be captured by graph properties derived from the text. Focusing on student essays, we represent the essay as a graph, and encode a variety of graph properties including PageRank as features for modeling essay scores related to quality of development. We demonstrate that our approach improves on a state-of-the-art system on the task of holistic scoring of persuasive essays and on the task of scoring narrative essays along the development dimension.
Selective Co-occurrences for Word-Emotion Association
Authors: Ameeta Agrawal, Aijun An
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Emotion Classification, Emotion Recognition, Word Embeddings

Emotion classification from text typically requires some degree of word-emotion association, either gathered from pre-existing emotion lexicons or calculated using some measure of semantic relatedness. Most emotion lexicons contain a fixed number of emotion categories and provide a rather limited coverage. Current measures of computing semantic relatedness, on the other hand, do not adapt well to the specific task of word-emotion association and therefore, yield average results. In this work, we propose an unsupervised method of learning word-emotion association from large text corpora, called Selective Co-occurrences (SECO), by leveraging the property of mutual exclusivity generally exhibited by emotions. Extensive evaluation, using just one seed word per emotion category, indicates the effectiveness of the proposed approach over three emotion lexicons and two state-of-the-art models of word embeddings on three datasets from different domains.
Weighted Neural Bag-of-n-grams Model: New Baselines for Text Classification
Authors: Bofang Li, Zhe Zhao, Tao Liu, Puwei Wang, Xiaoyong Du
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: General Classification, text-classification, Text Classification, Word Embeddings

NBSVM is one of the most popular methods for text classification and has been widely used as baselines for various text representation approaches. It uses Naive Bayes (NB) feature to weight sparse bag-of-n-grams representation. N-gram captures word order in short context and NB feature assigns more weights to those important words. However, NBSVM suffers from sparsity problem and is reported to be exceeded by newly proposed distributed (dense) text representations learned by neural networks. In this paper, we transfer the n-grams and NB weighting to neural models. We train n-gram embeddings and use NB weighting to guide the neural models to focus on important words. In fact, our methods can be viewed as distributed (dense) counterparts of sparse bag-of-n-grams in NBSVM. We discover that n-grams and NB weighting are also effective in distributed representations. As a result, our models achieve new strong baselines on 9 text classification datasets, e.g. on IMDB dataset, we reach performance of 93.5{\%} accuracy, which exceeds previous state-of-the-art results obtained by deep neural models. All source codes are publicly available at \url{https://github.com/zhezhaoa/neural_BOW_toolkit}.
Exploring Distributional Representations and Machine Translation for Aspect-based Cross-lingual Sentiment Classification.
Authors: Jeremy Barnes, Patrik Lambert, Toni Badia
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Cross-Lingual Sentiment Classification, Denoising, General Classification, Machine Translation, Sentence, Sentiment Analysis, Sentiment Classification, Translation, Word Embeddings, Zero-Shot Learning

Cross-lingual sentiment classification (CLSC) seeks to use resources from a source language in order to detect sentiment and classify text in a target language. Almost all research into CLSC has been carried out at sentence and document level, although this level of granularity is often less useful. This paper explores methods for performing aspect-based cross-lingual sentiment classification (aspect-based CLSC) for under-resourced languages. Given the limited nature of parallel data for many languages, we would like to make the most of this resource for our task. We compare zero-shot learning, bilingual word embeddings, stacked denoising autoencoder representations and machine translation techniques for aspect-based CLSC. Each of these approaches requires differing amounts of parallel data. We show that models based on distributed semantics can achieve comparable results to machine translation on aspect-based CLSC and give an analysis of the errors found for each method.
A Bilingual Attention Network for Code-switched Emotion Prediction
Authors: Zhongqing Wang, Yue Zhang, Sophia Lee, Shoushan Li, Guodong Zhou
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Emotion Recognition

Emotions in code-switching text can be expressed in either monolingual or bilingual forms. However, relatively little research has emphasized on code-switching text. In this paper, we propose a Bilingual Attention Network (BAN) model to aggregate the monolingual and bilingual informative words to form vectors from the document representation, and integrate the attention vectors to predict the emotion. The experiments show that the effectiveness of the proposed model. Visualization of the attention layers illustrates that the model selects qualitatively informative words.
UTCNN: a Deep Learning Model of Stance Classification on Social Media Text
Authors: Wei-Fan Chen, Lun-Wei Ku
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Document Classification, General Classification, Stance Classification, Text Classification

Most neural network models for document classification on social media focus on text information to the neglect of other information on these platforms. In this paper, we classify post stance on social media channels and develop UTCNN, a neural network model that incorporates user tastes, topic tastes, and user comments on posts. UTCNN not only works on social media texts, but also analyzes texts in forums and message boards. Experiments performed on Chinese Facebook data and English online debate forum data show that UTCNN achieves a 0.755 macro average f-score for supportive, neutral, and unsupportive stance classes on Facebook data, which is significantly better than models in which either user, topic, or comment information is withheld. This model design greatly mitigates the lack of data for the minor class. In addition, UTCNN yields a 0.842 accuracy on English online debate forum data, which also significantly outperforms results from previous work, showing that UTCNN performs well regardless of language or platform.
The Role of Intrinsic Motivation in Artificial Language Emergence: a Case Study on Colour
Authors: Miquel Cornudella, Thierry Poibeau, Remi van Trijp
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Artificial Life

Human languages have multiple strategies that allow us to discriminate objects in a vast variety of contexts. Colours have been extensively studied from this point of view. In particular, previous research in artificial language evolution has shown how artificial languages may emerge based on specific strategies to distinguish colours. Still, it has not been shown how several strategies of diverse complexity can be autonomously managed by artificial agents . We propose an intrinsic motivation system that allows agents in a population to create a shared artificial language and progressively increase its expressive power. Our results show that with such a system agents successfully regulate their language development, which indicates a relation between population size and consistency in the emergent communicative systems.
Predicting the Evocation Relation between Lexicalized Concepts
Authors: Yoshihiko Hayashi
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Relation

Evocation is a directed yet weighted semantic relationship between lexicalized concepts. Although evocation relations are considered potentially useful in several semantic NLP tasks, the prediction of the evocation relation between an arbitrary pair of concepts remains difficult, since evocation relationships cover a broader range of semantic relations rooted in human perception and experience. This paper presents a supervised learning approach to predict the strength (by regression) and to determine the directionality (by classification) of the evocation relation that might hold between a pair of lexicalized concepts. Empirical results that were obtained by investigating useful features are shown, indicating that a combination of the proposed features largely outperformed individual baselines, and also suggesting that semantic relational vectors computed from existing semantic vectors for lexicalized concepts were indeed effective for both the prediction of strength and the determination of directionality.
Collecting and Exploring Everyday Language for Predicting Psycholinguistic Properties of Words
Authors: Gustavo Paetzold, Lucia Specia
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Text Simplification

Exploring language usage through frequency analysis in large corpora is a defining feature in most recent work in corpus and computational linguistics. From a psycholinguistic perspective, however, the corpora used in these contributions are often not representative of language usage: they are either domain-specific, limited in size, or extracted from unreliable sources. In an effort to address this limitation, we introduce SubIMDB, a corpus of everyday language spoken text we created which contains over 225 million words. The corpus was extracted from 38,102 subtitles of family, comedy and children movies and series, and is the first sizeable structured corpus of subtitles made available. Our experiments show that word frequency norms extracted from this corpus are more effective than those from well-known norms such as Kucera-Francis, HAL and SUBTLEXus in predicting various psycholinguistic properties of words, such as lexical decision times, familiarity, age of acquisition and simplicity. We also provide evidence that contradict the long-standing assumption that the ideal size for a corpus can be determined solely based on how well its word frequencies correlate with lexical decision times.
Using Argument Mining to Assess the Argumentation Quality of Essays
Authors: Henning Wachsmuth, Khalid Al-Khatib, Benno Stein
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Argument Mining

Argument mining aims to determine the argumentative structure of texts. Although it is said to be crucial for future applications such as writing support systems, the benefit of its output has rarely been evaluated. This paper puts the analysis of the output into the focus. In particular, we investigate to what extent the mined structure can be leveraged to assess the argumentation quality of persuasive essays. We find insightful statistical patterns in the structure of essays. From these, we derive novel features that we evaluate in four argumentation-related essay scoring tasks. Our results reveal the benefit of argument mining for assessing argumentation quality. Among others, we improve the state of the art in scoring an essay{'}s organization and its argument strength.
Towards Time-Aware Knowledge Graph Completion
Authors: Tingsong Jiang, Tianyu Liu, Tao Ge, Lei Sha, Baobao Chang, Sujian Li, Zhifang Sui
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Knowledge Graph Completion, Knowledge Graphs, Question Answering, Relation Extraction, valid

Knowledge graph (KG) completion adds new facts to a KG by making inferences from existing facts. Most existing methods ignore the time information and only learn from time-unknown fact triples. In dynamic environments that evolve over time, it is important and challenging for knowledge graph completion models to take into account the temporal aspects of facts. In this paper, we present a novel time-aware knowledge graph completion model that is able to predict links in a KG using both the existing facts and the temporal information of the facts. To incorporate the happening time of facts, we propose a time-aware KG embedding model using temporal order information among facts. To incorporate the valid time of facts, we propose a joint time-aware inference model based on Integer Linear Programming (ILP) using temporal consistencyinformationasconstraints. Wefurtherintegratetwomodelstomakefulluseofglobal temporal information. We empirically evaluate our models on time-aware KG completion task. Experimental results show that our time-aware models achieve the state-of-the-art on temporal facts consistently.
Learning to Weight Translations using Ordinal Linear Regression and Query-generated Training Data for Ad-hoc Retrieval with Long Queries
Authors: Javid Dadashkarimi, Masoud Jalili Sabet, Azadeh Shakery
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Document Ranking, Information Retrieval, Learning-To-Rank, regression, Retrieval, Translation

Ordinal regression which is known with learning to rank has long been used in information retrieval (IR). Learning to rank algorithms, have been tailored in document ranking, information filtering, and building large aligned corpora successfully. In this paper, we propose to use this algorithm for query modeling in cross-language environments. To this end, first we build a query-generated training data using pseudo-relevant documents to the query and all translation candidates. The pseudo-relevant documents are obtained by top-ranked documents in response to a translation of the original query. The class of each candidate in the training data is determined based on presence/absence of the candidate in the pseudo-relevant documents. We learn an ordinal regression model to score the candidates based on their relevance to the context of the query, and after that, we construct a query-dependent translation model using a softmax function. Finally, we re-weight the query based on the obtained model. Experimental results on French, German, Spanish, and Italian CLEF collections demonstrate that the proposed method achieves better results compared to state-of-the-art cross-language information retrieval methods, particularly in long queries with large training data.
Neural Attention for Learning to Rank Questions in Community Question Answering
Authors: Salvatore Romeo, Giovanni Da San Martino, Alberto Barr{\'o}n-Cede{\~n}o, Aless Moschitti, ro, Yonatan Belinkov, Wei-Ning Hsu, Yu Zhang, Mitra Mohtarami, James Glass
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Community Question Answering, Learning-To-Rank, Natural Language Inference, Position, Question Answering, Reranking, Retrieval

In real-world data, e.g., from Web forums, text is often contaminated with redundant or irrelevant content, which leads to introducing noise in machine learning algorithms. In this paper, we apply Long Short-Term Memory networks with an attention mechanism, which can select important parts of text for the task of similar question retrieval from community Question Answering (cQA) forums. In particular, we use the attention weights for both selecting entire sentences and their subparts, i.e., word/chunk, from shallow syntactic trees. More interestingly, we apply tree kernels to the filtered text representations, thus exploiting the implicit features of the subtree space for learning question reranking. Our results show that the attention-based pruning allows for achieving the top position in the cQA challenge of SemEval 2016, with a relatively large gap from the other participants while greatly decreasing running time.
Modeling topic dependencies in semantically coherent text spans with copulas
Authors: Georgios Balikas, Hesam Amoualian, Marianne Clausel, Eric Gaussier, Massih R. Amini
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Topic Models

The exchangeability assumption in topic models like Latent Dirichlet Allocation (LDA) often results in inferring inconsistent topics for the words of text spans like noun-phrases, which are usually expected to be topically coherent. We propose copulaLDA, that extends LDA by integrating part of the text structure to the model and relaxes the conditional independence assumption between the word-specific latent topics given the per-document topic distributions. To this end, we assume that the words of text spans like noun-phrases are topically bound and we model this dependence with copulas. We demonstrate empirically the effectiveness of copulaLDA on both intrinsic and extrinsic evaluation tasks on several publicly available corpora.
Semantic Annotation Aggregation with Conditional Crowdsourcing Models and Word Embeddings
Authors: Paul Felt, Eric Ringger, Kevin Seppi
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: text annotation, Word Embeddings

In modern text annotation projects, crowdsourced annotations are often aggregated using item response models or by majority vote. Recently, item response models enhanced with generative data models have been shown to yield substantial benefits over those with conditional or no data models. However, suitable generative data models do not exist for many tasks, such as semantic labeling tasks. When no generative data model exists, we demonstrate that similar benefits may be derived by conditionally modeling documents that have been previously embedded in a semantic space using recent work in vector space models. We use this approach to show state-of-the-art results on a variety of semantic annotation aggregation tasks.