pwc-archive/papers-with-abstracts huggingface dataset:
Weakly-supervised text-to-speech alignment confidence measure
Authors: Guillaume Serri{\`e}re, Christophe Cerisara, Dominique Fohr, Odile Mella
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: speech-recognition, Speech Recognition, Speech Synthesis, text-to-speech, Text to Speech

This work proposes a new confidence measure for evaluating text-to-speech alignment systems outputs, which is a key component for many applications, such as semi-automatic corpus anonymization, lips syncing, film dubbing, corpus preparation for speech synthesis and speech recognition acoustic models training. This confidence measure exploits deep neural networks that are trained on large corpora without direct supervision. It is evaluated on an open-source spontaneous speech corpus and outperforms a confidence score derived from a state-of-the-art text-to-speech aligner. We further show that this confidence measure can be used to fine-tune the output of this aligner and improve the quality of the resulting alignment.
Domainless Adaptation by Constrained Decoding on a Schema Lattice
Authors: Young-Bum Kim, Karl Stratos, Ruhi Sarikaya
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Multi-Label Classification, Spoken Language Understanding

In many applications such as personal digital assistants, there is a constant need for new domains to increase the system{'}s coverage of user queries. A conventional approach is to learn a separate model every time a new domain is introduced. This approach is slow, inefficient, and a bottleneck for scaling to a large number of domains. In this paper, we introduce a framework that allows us to have a single model that can handle all domains: including unknown domains that may be created in the future as long as they are covered in the master schema. The key idea is to remove the need for distinguishing domains by explicitly predicting the schema of queries. Given permitted schema of a query, we perform constrained decoding on a lattice of slot sequences allowed under the schema. The proposed model achieves competitive and often superior performance over the conventional model trained separately per domain.
Sub-Word Similarity based Search for Embeddings: Inducing Rare-Word Embeddings for Word Similarity Tasks and Language Modelling
Authors: Mittul Singh, Clayton Greenberg, Youssef Oualil, Dietrich Klakow
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Language Modeling, Language Modelling, Morphological Analysis, Word Embeddings, Word Similarity

Training good word embeddings requires large amounts of data. Out-of-vocabulary words will still be encountered at test-time, leaving these words without embeddings. To overcome this lack of embeddings for rare words, existing methods leverage morphological features to generate embeddings. While the existing methods use computationally-intensive rule-based (Soricut and Och, 2015) or tool-based (Botha and Blunsom, 2014) morphological analysis to generate embeddings, our system applies a computationally-simpler sub-word search on words that have existing embeddings. Embeddings of the sub-word search results are then combined using string similarity functions to generate rare word embeddings. We augmented pre-trained word embeddings with these novel embeddings and evaluated on a rare word similarity task, obtaining up to 3 times improvement in correlation over the original set of embeddings. Applying our technique to embeddings trained on larger datasets led to on-par performance with the existing state-of-the-art for this task. Additionally, while analysing augmented embeddings in a log-bilinear language model, we observed up to 50{\%} reduction in rare word perplexity in comparison to other more complex language models.
Semi-automatic Detection of Cross-lingual Marketing Blunders based on Pragmatic Label Propagation in Wiktionary
Authors: Christian M. Meyer, Judith Eckle-Kohler, Iryna Gurevych
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Marketing

We introduce the task of detecting cross-lingual marketing blunders, which occur if a trade name resembles an inappropriate or negatively connotated word in a target language. To this end, we suggest a formal task definition and a semi-automatic method based the propagation of pragmatic labels from Wiktionary across sense-disambiguated translations. Our final tool assists users by providing clues for problematic names in any language, which we simulate in two experiments on detecting previously occurred marketing blunders and identifying relevant clues for established international brands. We conclude the paper with a suggested research roadmap for this new task. To initiate further research, we publish our online demo along with the source code and data at \url{http://uby.ukp.informatik.tu-darmstadt.de/blunder/}.
Ambient Search: A Document Retrieval System for Speech Streams
Authors: Benjamin Milde, Jonas Wacker, Stefan Radomski, Max M{\"u}hlh{\"a}user, Chris Biemann
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Articles, Information Retrieval, Keyphrase Generation, Retrieval, Speech Recognition

We present Ambient Search, an open source system for displaying and retrieving relevant documents in real time for speech input. The system works ambiently, that is, it unobstructively listens to speech streams in the background, identifies keywords and keyphrases for query construction and continuously serves relevant documents from its index. Query terms are ranked with Word2Vec and TF-IDF and are continuously updated to allow for ongoing querying of a document collection. The retrieved documents, in our case Wikipedia articles, are visualized in real time in a browser interface. Our evaluation shows that Ambient Search compares favorably to another implicit information retrieval system on speech streams. Furthermore, we extrinsically evaluate multiword keyphrase generation, showing positive impact for manual transcriptions.
Semi-supervised Gender Classification with Joint Textual and Social Modeling
Authors: Shoushan Li, Bin Dai, ZhengXian Gong, Guodong Zhou
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Classification, Gender Classification, General Classification

In gender classification, labeled data is often limited while unlabeled data is ample. This motivates semi-supervised learning for gender classification to improve the performance by exploring the knowledge in both labeled and unlabeled data. In this paper, we propose a semi-supervised approach to gender classification by leveraging textual features and a specific kind of indirect links among the users which we call {``}same-interest{''} links. Specifically, we propose a factor graph, namely Textual and Social Factor Graph (TSFG), to model both the textual and the {``}same-interest{''} link information. Empirical studies demonstrate the effectiveness of the proposed approach to semi-supervised gender classification.
Predicting proficiency levels in learner writings by transferring a linguistic complexity model from expert-written coursebooks
Authors: Ildik{\'o} Pil{\'a}n, Elena Volodina, Torsten Zesch
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Domain Adaptation, Language Acquisition, Transfer Learning

The lack of a sufficient amount of data tailored for a task is a well-recognized problem for many statistical NLP methods. In this paper, we explore whether data sparsity can be successfully tackled when classifying language proficiency levels in the domain of learner-written output texts. We aim at overcoming data sparsity by incorporating knowledge in the trained model from another domain consisting of input texts written by teaching professionals for learners. We compare different domain adaptation techniques and find that a weighted combination of the two types of data performs best, which can even rival systems based on considerably larger amounts of in-domain data. Moreover, we show that normalizing errors in learners{'} texts can substantially improve classification when level-annotated in-domain data is not available.
User Classification with Multiple Textual Perspectives
Authors: Dong Zhang, Shoushan Li, Hongling Wang, Guodong Zhou
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Age Classification, Classification, Gender Classification, General Classification, Recommendation Systems

Textual information is of critical importance for automatic user classification in social media. However, most previous studies model textual features in a single perspective while the text in a user homepage typically possesses different styles of text, such as original message and comment from others. In this paper, we propose a novel approach, namely ensemble LSTM, to user classification by incorporating multiple textual perspectives. Specifically, our approach first learns a LSTM representation with a LSTM recurrent neural network and then presents a joint learning method to integrating all naturally-divided textual perspectives. Empirical studies on two basic user classification tasks, i.e., gender classification and age classification, demonstrate the effectiveness of the proposed approach to user classification with multiple textual perspectives.
Says Who\ldots? Identification of Expert versus Layman Critics' Reviews of Documentary Films
Authors: Ming Jiang, Jana Diesner
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Decision Making, Diversity, Recommendation Systems

We extend classic review mining work by building a binary classifier that predicts whether a review of a documentary film was written by an expert or a layman with 90.70{\%} accuracy (F1 score), and compare the characteristics of the predicted classes. A variety of standard lexical and syntactic features was used for this supervised learning task. Our results suggest that experts write comparatively lengthier and more detailed reviews that feature more complex grammar and a higher diversity in their vocabulary. Layman reviews are more subjective and contextualized in peoples{'} everyday lives. Our error analysis shows that laymen are about twice as likely to be mistaken as experts than vice versa. We argue that the type of author might be a useful new feature for improving the accuracy of predicting the rating, helpfulness and authenticity of reviews. Finally, the outcomes of this work might help researchers and practitioners in the field of impact assessment to gain a more fine-grained understanding of the perception of different types of media consumers and reviewers of a topic, genre or information product.
Knowledge-Driven Event Embedding for Stock Prediction
Authors: Xiao Ding, Yue Zhang, Ting Liu, Junwen Duan
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Information Retrieval, Open Information Extraction, Prediction, Semantic Parsing, Stock Market Prediction, Stock Prediction, Tensor Networks, Word Embeddings

Representing structured events as vectors in continuous space offers a new way for defining dense features for natural language processing (NLP) applications. Prior work has proposed effective methods to learn event representations that can capture syntactic and semantic information over text corpus, demonstrating their effectiveness for downstream tasks such as event-driven stock prediction. On the other hand, events extracted from raw texts do not contain background knowledge on entities and relations that they are mentioned. To address this issue, this paper proposes to leverage extra information from knowledge graph, which provides ground truth such as attributes and properties of entities and encodes valuable relations between entities. Specifically, we propose a joint model to combine knowledge graph information into the objective function of an event embedding learning model. Experiments on event similarity and stock market prediction show that our model is more capable of obtaining better event embeddings and making more accurate prediction on stock market volatilities.
Distributed Representations for Building Profiles of Users and Items from Text Reviews
Authors: Wenliang Chen, Zhenjie Zhang, Zhenghua Li, Min Zhang
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Collaborative Filtering, Decision Making, Matrix Completion, Opinion Mining, Recommendation Systems

In this paper, we propose an approach to learn distributed representations of users and items from text comments for recommendation systems. Traditional recommendation algorithms, e.g. collaborative filtering and matrix completion, are not designed to exploit the key information hidden in the text comments, while existing opinion mining methods do not provide direct support to recommendation systems with useful features on users and items. Our approach attempts to construct vectors to represent profiles of users and items under a unified framework to maximize word appearance likelihood. Then, the vector representations are used for a recommendation task in which we predict scores on unobserved user-item pairs without given texts. The recommendation-aware distributed representation approach is fully supported by effective and efficient learning algorithms over massive text archive. Our empirical evaluations on real datasets show that our system outperforms the state-of-the-art baseline systems.
Improving Statistical Machine Translation with Selectional Preferences
Authors: Haiqing Tang, Deyi Xiong, Min Zhang, ZhengXian Gong
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Machine Translation, Semantic Role Labeling, Translation, Word Sense Disambiguation

Long-distance semantic dependencies are crucial for lexical choice in statistical machine translation. In this paper, we study semantic dependencies between verbs and their arguments by modeling selectional preferences in the context of machine translation. We incorporate preferences that verbs impose on subjects and objects into translation. In addition, bilingual selectional preferences between source-side verbs and target-side arguments are also investigated. Our experiments on Chinese-to-English translation tasks with large-scale training data demonstrate that statistical machine translation using verbal selectional preferences can achieve statistically significant improvements over a state-of-the-art baseline.
Hierarchical Permutation Complexity for Word Order Evaluation
Authors: Milo{\v{s}} Stanojevi{\'c}, Khalil Sima{'}an
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Machine Translation, Translation

Existing approaches for evaluating word order in machine translation work with metrics computed directly over a permutation of word positions in system output relative to a reference translation. However, every permutation factorizes into a permutation tree (PET) built of primal permutations, i.e., atomic units that do not factorize any further. In this paper we explore the idea that permutations factorizing into (on average) shorter primal permutations should represent simpler ordering as well. Consequently, we contribute Permutation Complexity, a class of metrics over PETs and their extension to forests, and define tight metrics, a sub-class of metrics implementing this idea. Subsequently we define example tight metrics and empirically test them in word order evaluation. Experiments on the WMT13 data sets for ten language pairs show that a tight metric is more often than not better than the baselines.
Get Semantic With Me! The Usefulness of Different Feature Types for Short-Answer Grading
Authors: Ulrike Pad{\'o}
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Natural Language Inference, text similarity

Automated short-answer grading is key to help close the automation loop for large-scale, computerised testing in education. A wide range of features on different levels of linguistic processing has been proposed so far. We investigate the relative importance of the different types of features across a range of standard corpora (both from a language skill and content assessment context, in English and in German). We find that features on the lexical, text similarity and dependency level often suffice to approximate full-model performance. Features derived from semantic processing particularly benefit the linguistically more varied answers in content assessment corpora.
Automatically Processing Tweets from Gang-Involved Youth: Towards Detecting Loss and Aggression
Authors: Terra Blevins, Robert Kwiatkowski, Jamie MacBeth, Kathleen McKeown, Desmond Patton, Owen Rambow
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12

Violence is a serious problems for cities like Chicago and has been exacerbated by the use of social media by gang-involved youths for taunting rival gangs. We present a corpus of tweets from a young and powerful female gang member and her communicators, which we have annotated with discourse intention, using a deep read to understand how and what triggered conversations to escalate into aggression. We use this corpus to develop a part-of-speech tagger and phrase table for the variant of English that is used and a classifier for identifying tweets that express grieving and aggression.
Content-based Influence Modeling for Opinion Behavior Prediction
Authors: Chengyao Chen, Zhitao Wang, Yu Lei, Wenjie Li
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Prediction

Nowadays, social media has become a popular platform for companies to understand their customers. It provides valuable opportunities to gain new insights into how a person{'}s opinion about a product is influenced by his friends. Though various approaches have been proposed to study the opinion formation problem, they all formulate opinions as the derived sentiment values either discrete or continuous without considering the semantic information. In this paper, we propose a Content-based Social Influence Model to study the implicit mechanism underlying the change of opinions. We then apply the learned model to predict users{'} future opinions. The advantages of the proposed model is the ability to handle the semantic information and to learn two influence components including the opinion influence of the content information and the social relation factors. In the experiments conducted on Twitter datasets, our model significantly outperforms other popular opinion formation models.
Data-driven learning of symbolic constraints for a log-linear model in a phonological setting
Authors: Gabriel Doyle, Roger Levy
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Machine Translation

We propose a non-parametric Bayesian model for learning and weighting symbolically-defined constraints to populate a log-linear model. The model jointly infers a vector of binary constraint values for each candidate output and likely definitions for these constraints, combining observations of the output classes with a (potentially infinite) grammar over potential constraint definitions. We present results on a small morphophonological system, English regular plurals, as a test case. The inferred constraints, based on a grammar of articulatory features, perform as well as theoretically-defined constraints on both observed and novel forms of English regular plurals. The learned constraint values and definitions also closely resemble standard constraints defined within phonological theory.
Chinese Tense Labelling and Causal Analysis
Authors: Hen-Hsen Huang, Chang-Rui Yang, Hsin-Hsi Chen
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: General Classification, Question Answering

This paper explores the role of tense information in Chinese causal analysis. Both tasks of causal type classification and causal directionality identification are experimented to show the significant improvement gained from tense features. To automatically extract the tense features, a Chinese tense predictor is proposed. Based on large amount of parallel data, our semi-supervised approach improves the dependency-based convolutional neural network (DCNN) models for Chinese tense labelling and thus the causal analysis.
Exploring Topic Discriminating Power of Words in Latent Dirichlet Allocation
Authors: Kai Yang, Yi Cai, Zhenhong Chen, Ho-fung Leung, Raymond Lau
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Topic Models

Latent Dirichlet Allocation (LDA) and its variants have been widely used to discover latent topics in textual documents. However, some of topics generated by LDA may be noisy with irrelevant words scattering across these topics. We name this kind of words as topic-indiscriminate words, which tend to make topics more ambiguous and less interpretable by humans. In our work, we propose a new topic model named TWLDA, which assigns low weights to words with low topic discriminating power (ability). Our experimental results show that the proposed approach, which effectively reduces the number of topic-indiscriminate words in discovered topics, improves the effectiveness of LDA.
plWordNet 3.0 -- a Comprehensive Lexical-Semantic Resource
Authors: Marek Maziarz, Maciej Piasecki, Ewa Rudnicka, Stan Szpakowicz, Pawe{\l} K{\k{e}}dzia
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12

We have released plWordNet 3.0, a very large wordnet for Polish. In addition to what is expected in wordnets {--} richly interrelated synsets {--} it contains sentiment and emotion annotations, a large set of multi-word expressions, and a mapping onto WordNet 3.1. Part of the release is enWordNet 1.0, a substantially enlarged copy of WordNet 3.1, with material added to allow for a more complete mapping. The paper discusses the design principles of plWordNet, its content, its statistical portrait, a comparison with similar resources, and a partial list of applications.
Time-Independent and Language-Independent Extraction of Multiword Expressions From Twitter
Authors: Nikhil Londhe, Rohini Srihari, Vishrawas Gopalakrishnan
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Sentiment Analysis

Multiword Expressions (MWEs) are crucial lexico-semantic units in any language. However, most work on MWEs has been focused on standard monolingual corpora. In this work, we examine MWE usage on Twitter - an inherently multilingual medium with an extremely short average text length that is often replete with grammatical errors. In this work we present a new graph based, language agnostic method for automatically extracting MWEs from tweets. We show how our method outperforms standard Association Measures. We also present a novel unsupervised evaluation technique to ascertain the accuracy of MWE extraction.
Incremental Global Event Extraction
Authors: Alex Judea, Michael Strube
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Event Extraction, Relation Extraction, Sentence, Word Sense Disambiguation

Event extraction is a difficult information extraction task. Li et al. (2014) explore the benefits of modeling event extraction and two related tasks, entity mention and relation extraction, jointly. This joint system achieves state-of-the-art performance in all tasks. However, as a system operating only at the sentence level, it misses valuable information from other parts of the document. In this paper, we present an incremental easy-first approach to make the global context of the entire document available to the intra-sentential, state-of-the-art event extractor. We show that our method robustly increases performance on two datasets, namely ACE 2005 and TAC 2015.
Revisiting Taxonomy Induction over Wikipedia
Authors: Amit Gupta, Francesco Piccinno, Mikhail Kozhevnikov, Marius Pa{\c{s}}ca, Daniele Pighin
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Information Retrieval

Guided by multiple heuristics, a unified taxonomy of entities and categories is distilled from the Wikipedia category network. A comprehensive evaluation, based on the analysis of upward generalization paths, demonstrates that the taxonomy supports generalizations which are more than twice as accurate as the state of the art. The taxonomy is available at \url{http://headstaxonomy.com}.