pwc-archive/papers-with-abstracts huggingface dataset:
Syntactic realization with data-driven neural tree grammars
Authors: Brian McMahan, Matthew Stone
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Language Modeling, Language Modelling, Text Generation

A key component in surface realization in natural language generation is to choose concrete syntactic relationships to express a target meaning. We develop a new method for syntactic choice based on learning a stochastic tree grammar in a neural architecture. This framework can exploit state-of-the-art methods for modeling word sequences and generalizing across vocabulary. We also induce embeddings to generalize over elementary tree structures and exploit a tree recurrence over the input structure to model long-distance influences between NLG choices. We evaluate the models on the task of linearizing unannotated dependency trees, documenting the contribution of our modeling techniques to improvements in both accuracy and run time.
Abstractive News Summarization based on Event Semantic Link Network
Authors: Wei Li, Lei He, Hai Zhuge
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Abstractive Text Summarization, Document Summarization, Multi-Document Summarization, News Summarization

This paper studies the abstractive multi-document summarization for event-oriented news texts through event information extraction and abstract representation. Fine-grained event mentions and semantic relations between them are extracted to build a unified and connected event semantic link network, an abstract representation of source texts. A network reduction algorithm is proposed to summarize the most salient and coherent event information. New sentences with good linguistic quality are automatically generated and selected through sentences over-generation and greedy-selection processes. Experimental results on DUC 2006 and DUC 2007 datasets show that our system significantly outperforms the state-of-the-art extractive and abstractive baselines under both pyramid and ROUGE evaluation metrics.
A General Optimization Framework for Multi-Document Summarization Using Genetic Algorithms and Swarm Intelligence
Authors: Maxime Peyrard, Judith Eckle-Kohler
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Document Summarization, Multi-Document Summarization

Extracting summaries via integer linear programming and submodularity are popular and successful techniques in extractive multi-document summarization. However, many interesting optimization objectives are neither submodular nor factorizable into an integer linear program. We address this issue and present a general optimization framework where any function of input documents and a system summary can be plugged in. Our framework includes two kinds of summarizers {--} one based on genetic algorithms, the other using a swarm intelligence approach. In our experimental evaluation, we investigate the optimization of two information-theoretic summary evaluation metrics and find that our framework yields competitive results compared to several strong summarization baselines. Our comparative analysis of the genetic and swarm summarizers reveals interesting complementary properties.
Predictive Incremental Parsing Helps Language Modeling
Authors: Arne K{\"o}hn, Timo Baumann
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Language Modeling, Language Modelling, Sentence

Predictive incremental parsing produces syntactic representations of sentences as they are produced, e.g. by typing or speaking. In order to generate connected parses for such unfinished sentences, upcoming word types can be hypothesized and structurally integrated with already realized words. For example, the presence of a determiner as the last word of a sentence prefix may indicate that a noun will appear somewhere in the completion of that sentence, and the determiner can be attached to the predicted noun. We combine the forward-looking parser predictions with backward-looking N-gram histories and analyze in a set of experiments the impact on language models, i.e. stronger discriminative power but also higher data sparsity. Conditioning N-gram models, MaxEnt models or RNN-LMs on parser predictions yields perplexity reductions of about 6{\%}. Our method (a) retains online decoding capabilities and (b) incurs relatively little computational overhead which sets it apart from previous approaches that use syntax for language modeling. Our method is particularly attractive for modular systems that make use of a syntax parser anyway, e.g. as part of an understanding pipeline where predictive parsing improves language modeling at no additional cost.
A Neural Attention Model for Disfluency Detection
Authors: Shaolei Wang, Wanxiang Che, Ting Liu
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Decoder, Information Retrieval, model, Sentence

In this paper, we study the problem of disfluency detection using the encoder-decoder framework. We treat disfluency detection as a sequence-to-sequence problem and propose a neural attention-based model which can efficiently model the long-range dependencies between words and make the resulting sentence more likely to be grammatically correct. Our model firstly encode the source sentence with a bidirectional Long Short-Term Memory (BI-LSTM) and then use the neural attention as a pointer to select an ordered sub sequence of the input as the output. Experiments show that our model achieves the state-of-the-art f-score of 86.7{\%} on the commonly used English Switchboard test set. We also evaluate the performance of our model on the in-house annotated Chinese data and achieve a significantly higher f-score compared to the baseline of CRF-based approach.
Detecting Sentence Boundaries in Sanskrit Texts
Authors: Oliver Hellwig
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Boundary Detection, Sentence

The paper applies a deep recurrent neural network to the task of sentence boundary detection in Sanskrit, an important, yet underresourced ancient Indian language. The deep learning approach improves the F scores set by a metrical baseline and by a Conditional Random Field classifier by more than 10{\%}.
Consistent Word Segmentation, Part-of-Speech Tagging and Dependency Labelling Annotation for Chinese Language
Authors: Mo Shen, Wingmui Li, HyunJeong Choe, Chenhui Chu, Daisuke Kawahara, Sadao Kurohashi
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Chinese Word Segmentation, Machine Translation, Morphological Analysis, Part-Of-Speech Tagging, POS, POS Tagging, Segmentation, Translation

In this paper, we propose a new annotation approach to Chinese word segmentation, part-of-speech (POS) tagging and dependency labelling that aims to overcome the two major issues in traditional morphology-based annotation: Inconsistency and data sparsity. We re-annotate the Penn Chinese Treebank 5.0 (CTB5) and demonstrate the advantages of this approach compared to the original CTB5 annotation through word segmentation, POS tagging and machine translation experiments.
A Word Labeling Approach to Thai Sentence Boundary Detection and POS Tagging
Authors: Nina Zhou, AiTi Aw, Nattadaporn Lertcheva, Xuancong Wang
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Boundary Detection, Machine Translation, Part-Of-Speech Tagging, POS, POS Tagging, Sentence

Previous studies on Thai Sentence Boundary Detection (SBD) mostly assumed sentence ends at a space disambiguation problem, which classified space either as an indicator for Sentence Boundary (SB) or non-Sentence Boundary (nSB). In this paper, we propose a word labeling approach which treats space as a normal word, and detects SB between any two words. This removes the restriction for SB to be oc-curred only at space and makes our system more robust for modern Thai writing. It is because in modern Thai writing, space is not consistently used to indicate SB. As syntactic information contributes to better SBD, we further propose a joint Part-Of-Speech (POS) tagging and SBD framework based on Factorial Conditional Random Field (FCRF) model. We compare the performance of our proposed ap-proach with reported methods on ORCHID corpus. We also performed experiments of FCRF model on the TaLAPi corpus. The results show that the word labelling approach has better performance than pre-vious space-based classification approaches and FCRF joint model outperforms LCRF model in terms of SBD in all experiments.
Assigning Fine-grained PoS Tags based on High-precision Coarse-grained Tagging
Authors: Tobias Horsmann, Torsten Zesch
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: POS, POS Tagging, TAG

We propose a new approach to PoS tagging where in a first step, we assign a coarse-grained tag corresponding to the main syntactic category. Based on this high-precision decision, in the second step we utilize specially trained fine-grained models with heavily reduced decision complexity. By analyzing the system under oracle conditions, we show that there is a quite large potential for significantly outperforming a competitive baseline. When we take error-propagation from the coarse-grained tagging into account, our approach is on par with the state of the art. Our approach also allows tailoring the tagger towards recognizing single word classes which are of interest e.g. for researchers searching for specific phenomena in large corpora. In a case study, we significantly outperform a standard model that also makes use of the same optimizations.
Data-Driven Morphological Analysis and Disambiguation for Morphologically Rich Languages and Universal Dependencies
Authors: Amir More, Reut Tsarfaty
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Morphological Analysis, TAG

Parsing texts into universal dependencies (UD) in realistic scenarios requires infrastructure for the morphological analysis and disambiguation (MA{\&}D) of typologically different languages as a first tier. MA{\&}D is particularly challenging in morphologically rich languages (MRLs), where the ambiguous space-delimited tokens ought to be disambiguated with respect to their constituent morphemes, each morpheme carrying its own tag and a rich set features. Here we present a novel, language-agnostic, framework for MA{\&}D, based on a transition system with two variants {---} word-based and morpheme-based {---} and a dedicated transition to mitigate the biases of variable-length morpheme sequences. Our experiments on a Modern Hebrew case study show state of the art results, and we show that the morpheme-based MD consistently outperforms our word-based variant. We further illustrate the utility and multilingual coverage of our framework by morphologically analyzing and disambiguating the large set of languages in the UD treebanks.
Automatic Syllabification for Manipuri language
Authors: Loitongbam Gyanendro Singh, Lenin Laitonjam, Sanasam Ranbir Singh
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Automatic Speech Recognition (ASR), Segmentation, Speech Recognition, Speech Synthesis, Text-To-Speech Synthesis

Development of hand crafted rule for syllabifying words of a language is an expensive task. This paper proposes several data-driven methods for automatic syllabification of words written in Manipuri language. Manipuri is one of the scheduled Indian languages. First, we propose a language-independent rule-based approach formulated using entropy based phonotactic segmentation. Second, we project the syllabification problem as a sequence labeling problem and investigate its effect using various sequence labeling approaches. Third, we combine the effect of sequence labeling and rule-based method and investigate the performance of the hybrid approach. From various experimental observations, it is evident that the proposed methods outperform the baseline rule-based method. The entropy based phonotactic segmentation provides a word accuracy of 96{\%}, CRF (sequence labeling approach) provides 97{\%} and hybrid approach provides 98{\%} word accuracy.
Continuous Expressive Speaking Styles Synthesis based on CVSM and MR-HMM
Authors: Jaime Lorenzo-Trueba, Roberto Barra-Chicote, Ascension Gallardo-Antolin, Junichi Yamagishi, Juan M. Montero
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Expressive Speech Synthesis, Speech Recognition, Speech Synthesis, text-to-speech, Text to Speech

This paper introduces a continuous system capable of automatically producing the most adequate speaking style to synthesize a desired target text. This is done thanks to a joint modeling of the acoustic and lexical parameters of the speaker models by adapting the CVSM projection of the training texts using MR-HMM techniques. As such, we consider that as long as sufficient variety in the training data is available, we should be able to model a continuous lexical space into a continuous acoustic space. The proposed continuous automatic text to speech system was evaluated by means of a perceptual evaluation in order to compare them with traditional approaches to the task. The system proved to be capable of conveying the correct expressiveness (average adequacy of 3.6) with an expressive strength comparable to oracle traditional expressive speech synthesis (average of 3.6) although with a drop in speech quality mainly due to the semi-continuous nature of the data (average quality of 2.9). This means that the proposed system is capable of improving traditional neutral systems without requiring any additional user interaction.
An Automatic Prosody Tagger for Spontaneous Speech
Authors: M{\'o}nica Dom{\'\i}nguez, Mireia Farr{\'u}s, Leo Wanner
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Descriptive

Speech prosody is known to be central in advanced communication technologies. However, despite the advances of theoretical studies in speech prosody, so far, no large scale prosody annotated resources that would facilitate empirical research and the development of empirical computational approaches are available. This is to a large extent due to the fact that current common prosody annotation conventions offer a descriptive framework of intonation contours and phrasing based on labels. This makes it difficult to reach a satisfactory inter-annotator agreement during the annotation of gold standard annotations and, subsequently, to create consistent large scale annotations. To address this problem, we present an annotation schema for prominence and boundary labeling of prosodic phrases based upon acoustic parameters and a tagger for prosody annotation at the prosodic phrase level. Evaluation proves that inter-annotator agreement reaches satisfactory values, from 0.60 to 0.80 Cohen{'}s kappa, while the prosody tagger achieves acceptable recall and f-measure figures for five spontaneous samples used in the evaluation of monologue and dialogue formats in English and Spanish. The work presented in this paper is a first step towards a semi-automatic acquisition of large corpora for empirical prosodic analysis.
Frustratingly Easy Neural Domain Adaptation
Authors: Young-Bum Kim, Karl Stratos, Ruhi Sarikaya
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Domain Adaptation

Popular techniques for domain adaptation such as the feature augmentation method of Daum{\'e} III (2009) have mostly been considered for sparse binary-valued features, but not for dense real-valued features such as those used in neural networks. In this paper, we describe simple neural extensions of these techniques. First, we propose a natural generalization of the feature augmentation method that uses K + 1 LSTMs where one model captures global patterns across all K domains and the remaining K models capture domain-specific information. Second, we propose a novel application of the framework for learning shared structures by Ando and Zhang (2005) to domain adaptation, and also provide a neural extension of their approach. In experiments on slot tagging over 17 domains, our methods give clear performance improvement over Daum{\'e} III (2009) applied on feature-rich CRFs.
A House United: Bridging the Script and Lexical Barrier between Hindi and Urdu
Authors: Riyaz A. Bhat, Irshad A. Bhat, Naman jain, Dipti Misra Sharma
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Dependency Parsing, Machine Translation, Part-Of-Speech Tagging, Sentence, Transliteration, Word Embeddings

In Computational Linguistics, Hindi and Urdu are not viewed as a monolithic entity and have received separate attention with respect to their text processing. From part-of-speech tagging to machine translation, models are separately trained for both Hindi and Urdu despite the fact that they represent the same language. The reasons mainly are their divergent literary vocabularies and separate orthographies, and probably also their political status and the social perception that they are two separate languages. In this article, we propose a simple but efficient approach to bridge the lexical and orthographic differences between Hindi and Urdu texts. With respect to text processing, addressing the differences between the Hindi and Urdu texts would be beneficial in the following ways: (a) instead of training separate models, their individual resources can be augmented to train single, unified models for better generalization, and (b) their individual text processing applications can be used interchangeably under varied resource conditions. To remove the script barrier, we learn accurate statistical transliteration models which use sentence-level decoding to resolve word ambiguity. Similarly, we learn cross-register word embeddings from the harmonized Hindi and Urdu corpora to nullify their lexical divergences. As a proof of the concept, we evaluate our approach on the Hindi and Urdu dependency parsing under two scenarios: (a) resource sharing, and (b) resource augmentation. We demonstrate that a neural network-based dependency parser trained on augmented, harmonized Hindi and Urdu resources performs significantly better than the parsing models trained separately on the individual resources. We also show that we can achieve near state-of-the-art results when the parsers are used interchangeably.
Deeper syntax for better semantic parsing
Authors: Olivier Michalon, Corentin Ribeyre, C, Marie ito, Alexis Nasr
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Semantic Parsing, Semantic Role Labeling, Sentence

Syntax plays an important role in the task of predicting the semantic structure of a sentence. But syntactic phenomena such as alternations, control and raising tend to obfuscate the relation between syntax and semantics. In this paper we predict the semantic structure of a sentence using a deeper syntax than what is usually done. This deep syntactic representation abstracts away from purely syntactic phenomena and proposes a structural organization of the sentence that is closer to the semantic representation. Experiments conducted on a French corpus annotated with semantic frames showed that a semantic parser reaches better performances with such a deep syntactic input.
Language Independent Dependency to Constituent Tree Conversion
Authors: Young-suk Lee, Zhiguo Wang
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12

We present a dependency to constituent tree conversion technique that aims to improve constituent parsing accuracies by leveraging dependency treebanks available in a wide variety in many languages. The technique works in two steps. First, a partial constituent tree is derived from a dependency tree with a very simple deterministic algorithm that is both language and dependency type independent. Second, a complete high accuracy constituent tree is derived with a constraint-based parser, which uses the partial constituent tree as external constraints. Evaluated on Section 22 of the WSJ Treebank, the technique achieves the state-of-the-art conversion F-score 95.6. When applied to English Universal Dependency treebank and German CoNLL2006 treebank, the converted treebanks added to the human-annotated constituent parser training corpus improve parsing F-scores significantly for both languages.
Promoting multiword expressions in A* TAG parsing
Authors: Jakub Waszczuk, Agata Savary, Yannick Parmentier
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: TAG

Multiword expressions (MWEs) are pervasive in natural languages and often have both idiomatic and compositional readings, which leads to high syntactic ambiguity. We show that for some MWE types idiomatic readings are usually the correct ones. We propose a heuristic for an A* parser for Tree Adjoining Grammars which benefits from this knowledge by promoting MWE-oriented analyses. This strategy leads to a substantial reduction in the parsing search space in case of true positive MWE occurrences, while avoiding parsing failures in case of false positives.
Incrementally Learning a Dependency Parser to Support Language Documentation in Field Linguistics
Authors: Morgan Ulinski, Julia Hirschberg, Owen Rambow
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Dependency Parsing, Sentence

We present experiments in incrementally learning a dependency parser. The parser will be used in the WordsEye Linguistics Tools (WELT) (Ulinski et al., 2014) which supports field linguists documenting a language{'}s syntax and semantics. Our goal is to make syntactic annotation faster for field linguists. We have created a new parallel corpus of descriptions of spatial relations and motion events, based on pictures and video clips used by field linguists for elicitation of language from native speaker informants. We collected descriptions for each picture and video from native speakers in English, Spanish, German, and Egyptian Arabic. We compare the performance of MSTParser (McDonald et al., 2006) and MaltParser (Nivre et al., 2006) when trained on small amounts of this data. We find that MaltParser achieves the best performance. We also present the results of experiments using the parser to assist with annotation. We find that even when the parser is trained on a single sentence from the corpus, annotation time significantly decreases.
Bitext Name Tagging for Cross-lingual Entity Annotation Projection
Authors: Dongxu Zhang, Boliang Zhang, Xiaoman Pan, Xiaocheng Feng, Heng Ji, Weiran Xu
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Word Alignment

Annotation projection is a practical method to deal with the low resource problem in incident languages (IL) processing. Previous methods on annotation projection mainly relied on word alignment results without any training process, which led to noise propagation caused by word alignment errors. In this paper, we focus on the named entity recognition (NER) task and propose a weakly-supervised framework to project entity annotations from English to IL through bitexts. Instead of directly relying on word alignment results, this framework combines advantages of rule-based methods and deep learning methods by implementing two steps: First, generates a high-confidence entity annotation set on IL side with strict searching methods; Second, uses this high-confidence set to weakly supervise the model training. The model is finally used to accomplish the projecting process. Experimental results on two low-resource ILs show that the proposed method can generate better annotations projected from English-IL parallel corpora. The performance of IL name tagger can also be improved significantly by training on the newly projected IL annotation set.