pwc-archive/papers-with-abstracts huggingface dataset:
FLORS: Fast and Simple Domain Adaptation for Part-of-Speech Tagging
Authors: Tobias Schnabel, Hinrich Sch{\"u}tze
Date: 2014-01-01 00:00:00
Proceeding: TACL 2014 1
Tasks: Domain Adaptation, Part-Of-Speech Tagging, Unsupervised Domain Adaptation

We present FLORS, a new part-of-speech tagger for domain adaptation. FLORS uses robust representations that work especially well for unknown words and for known words with unseen tags. FLORS is simpler and faster than previous domain adaptation methods, yet it has significantly better accuracy than several baselines.
Automatic Detection and Language Identification of Multilingual Documents
Authors: Marco Lui, Jey Han Lau, Timothy Baldwin
Date: 2014-01-01 00:00:00
Proceeding: TACL 2014 1
Tasks: Language Identification, Machine Translation

Language identification is the task of automatically detecting the language(s) present in a document based on the content of the document. In this work, we address the problem of detecting documents that contain text from more than one language (multilingual documents). We introduce a method that is able to detect that a document is multilingual, identify the languages present, and estimate their relative proportions. We demonstrate the effectiveness of our method over synthetic data, as well as real-world multilingual documents collected from the web.
A Crossing-Sensitive Third-Order Factorization for Dependency Parsing
Authors: Emily Pitler
Date: 2014-01-01 00:00:00
Proceeding: TACL 2014 1
Tasks: Dependency Parsing

Parsers that parametrize over wider scopes are generally more accurate than edge-factored models. For graph-based non-projective parsers, wider factorizations have so far implied large increases in the computational complexity of the parsing problem. This paper introduces a {``}crossing-sensitive{''} generalization of a third-order factorization that trades off complexity in the model structure (i.e., scoring with features over multiple edges) with complexity in the output structure (i.e., producing crossing edges). Under this model, the optimal 1-Endpoint-Crossing tree can be found in O(n4) time, matching the asymptotic run-time of both the third-order projective parser and the edge-factored 1-Endpoint-Crossing parser. The crossing-sensitive third-order parser is significantly more accurate than the third-order projective parser under many experimental settings and significantly less accurate on none.
Cross-lingual Projected Expectation Regularization for Weakly Supervised Learning
Authors: Mengqiu Wang, Christopher D. Manning
Date: 2014-01-01 00:00:00
Proceeding: TACL 2014 1
Tasks: NER, Weakly-supervised Learning

We consider a multilingual weakly supervised learning scenario where knowledge from annotated corpora in a resource-rich language is transferred via bitext to guide the learning in other languages. Past approaches project labels across bitext and use them as features or gold labels for training. We propose a new method that projects model expectations rather than labels, which facilities transfer of model uncertainty across language boundaries. We encode expectations as constraints and train a discriminative CRF model using Generalized Expectation Criteria (Mann and McCallum, 2010). Evaluated on standard Chinese-English and German-English NER datasets, our method demonstrates F1 scores of 64{\%} and 60{\%} when no labeled data is used. Attaining the same accuracy with supervised CRFs requires 12k and 1.5k labeled sentences. Furthermore, when combined with labeled examples, our method yields significant improvements over state-of-the-art supervised methods, achieving best reported numbers to date on Chinese OntoNotes and German CoNLL-03 datasets.
From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions
Authors: Peter Young, Alice Lai, Micah Hodosh, Julia Hockenmaier
Date: 2014-01-01 00:00:00
Proceeding: TACL 2014 1
Tasks: Descriptive, Semantic Textual Similarity

We propose to use the visual denotations of linguistic expressions (i.e. the set of images they describe) to define novel denotational similarity metrics, which we show to be at least as beneficial as distributional similarities for two tasks that require semantic inference. To compute these denotational similarities, we construct a denotation graph, i.e. a subsumption hierarchy over constituents and their denotations, based on a large corpus of 30K images and 150K descriptive captions.
The Language Demographics of Amazon Mechanical Turk
Authors: Ellie Pavlick, Matt Post, Ann Irvine, Dmitry Kachaev, Chris Callison-Burch
Date: 2014-01-01 00:00:00
Proceeding: TACL 2014 1
Tasks: Machine Translation, Translation

We present a large scale study of the languages spoken by bilingual workers on Mechanical Turk (MTurk). We establish a methodology for determining the language skills of anonymous crowd workers that is more robust than simple surveying. We validate workers{'} self-reported language skill claims by measuring their ability to correctly translate words, and by geolocating workers to see if they reside in countries where the languages are likely to be spoken. Rather than posting a one-off survey, we posted paid tasks consisting of 1,000 assignments to translate a total of 10,000 words in each of 100 languages. Our study ran for several months, and was highly visible on the MTurk crowdsourcing platform, increasing the chances that bilingual workers would complete it. Our study was useful both to create bilingual dictionaries and to act as census of the bilingual speakers on MTurk. We use this data to recommend languages with the largest speaker populations as good candidates for other researchers who want to develop crowdsourced, multilingual technologies. To further demonstrate the value of creating data via crowdsourcing, we hire workers to create bilingual parallel corpora in six Indian languages, and use them to train statistical machine translation systems.
Exploring the Role of Stress in Bayesian Word Segmentation using Adaptor Grammars
Authors: Benjamin B{\"o}rschinger, Mark Johnson
Date: 2014-01-01 00:00:00
Proceeding: TACL 2014 1
Tasks: Segmentation

Stress has long been established as a major cue in word segmentation for English infants. We show that enabling a current state-of-the-art Bayesian word segmentation model to take advantage of stress cues noticeably improves its performance. We find that the improvements range from 10 to 4{\%}, depending on both the use of phonotactic cues and, to a lesser extent, the amount of evidence available to the learner. We also find that in particular early on, stress cues are much more useful for our model than phonotactic cues by themselves, consistent with the finding that children do seem to use stress cues before they use phonotactic cues. Finally, we study how the model{'}s knowledge about stress patterns evolves over time. We not only find that our model correctly acquires the most frequent patterns relatively quickly but also that the Unique Stress Constraint that is at the heart of a previously proposed model does not need to be built in but can be acquired jointly with word segmentation.
Parallel Algorithms for Unsupervised Tagging
Authors: Sujith Ravi, Sergei Vassilivitskii, Vibhor Rastogi
Date: 2014-01-01 00:00:00
Proceeding: TACL 2014 1
Tasks: Part-Of-Speech Tagging

We propose a new method for unsupervised tagging that finds minimal models which are then further improved by Expectation Maximization training. In contrast to previous approaches that rely on manually specified and multi-step heuristics for model minimization, our approach is a simple greedy approximation algorithm DMLC (Distributed-Minimum-Label-Cover) that solves this objective in a single step. We extend the method and show how to efficiently parallelize the algorithm on modern parallel computing platforms while preserving approximation guarantees. The new method easily scales to large data and grammar sizes, overcoming the memory bottleneck in previous approaches. We demonstrate the power of the new algorithm by evaluating on various sequence labeling tasks: Part-of-Speech tagging for multiple languages (including low-resource languages), with complete and incomplete dictionaries, and supertagging, a complex sequence labeling task, where the grammar size alone can grow to millions of entries. Our results show that for all of these settings, our method achieves state-of-the-art scalable performance that yields high quality tagging outputs.
A Tabular Method for Dynamic Oracles in Transition-Based Parsing
Authors: Yoav Goldberg, Francesco Sartorio, Giorgio Satta
Date: 2014-01-01 00:00:00
Proceeding: TACL 2014 1
Tasks: ARC

We develop parsing oracles for two transition-based dependency parsers, including the arc-standard parser, solving a problem that was left open in (Goldberg and Nivre, 2013). We experimentally show that using these oracles during training yields superior parsing accuracies on many languages.
Joint Incremental Disfluency Detection and Dependency Parsing
Authors: Matthew Honnibal, Mark Johnson
Date: 2014-01-01 00:00:00
Proceeding: TACL 2014 1
Tasks: Dependency Parsing, Speech Recognition, Transition-Based Dependency Parsing

We present an incremental dependency parsing model that jointly performs disfluency detection. The model handles speech repairs using a novel non-monotonic transition system, and includes several novel classes of features. For comparison, we evaluated two pipeline systems, using state-of-the-art disfluency detectors. The joint model performed better on both tasks, with a parse accuracy of 90.5{\%} and 84.0{\%} accuracy at disfluency detection. The model runs in expected linear time, and processes over 550 tokens a second.
Temporal Annotation in the Clinical Domain
Authors: William F. Styler IV, Steven Bethard, Sean Finan, Martha Palmer, Sameer Pradhan, Piet C de Groen, Brad Erickson, Timothy Miller, Chen Lin, Guergana Savova, James Pustejovsky
Date: 2014-01-01 00:00:00
Proceeding: TACL 2014 1

This article discusses the requirements of a formal specification for the annotation of temporal information in clinical narratives. We discuss the implementation and extension of ISO-TimeML for annotating a corpus of clinical notes, known as the THYME corpus. To reflect the information task and the heavily inference-based reasoning demands in the domain, a new annotation guideline has been developed, {``}the THYME Guidelines to ISO-TimeML (THYME-TimeML){''}. To clarify what relations merit annotation, we distinguish between linguistically-derived and inferentially-derived temporal orderings in the text. We also apply a top performing TempEval 2013 system against this new resource to measure the difficulty of adapting systems to the clinical domain. The corpus is available to the community and has been proposed for use in a SemEval 2015 task.
Senti-LSSVM: Sentiment-Oriented Multi-Relation Extraction with Latent Structural SVM
Authors: Lizhen Qu, Yi Zhang, Rui Wang, Lili Jiang, Rainer Gemulla, Gerhard Weikum
Date: 2014-01-01 00:00:00
Proceeding: TACL 2014 1
Tasks: Marketing, Relation, Relation Extraction, Sentiment Analysis, Structured Prediction

Extracting instances of sentiment-oriented relations from user-generated web documents is important for online marketing analysis. Unlike previous work, we formulate this extraction task as a structured prediction problem and design the corresponding inference as an integer linear program. Our latent structural SVM based model can learn from training corpora that do not contain explicit annotations of sentiment-bearing expressions, and it can simultaneously recognize instances of both binary (polarity) and ternary (comparative) relations with regard to entity mentions of interest. The empirical evaluation shows that our approach significantly outperforms state-of-the-art systems across domains (cameras and movies) and across genres (reviews and forum posts). The gold standard corpus that we built will also be a valuable resource for the community.
Segmentation for Efficient Supervised Language Annotation with an Explicit Cost-Utility Tradeoff
Authors: Matthias Sperber, Mirjam Simantzik, Graham Neubig, Satoshi Nakamura, Alex Waibel
Date: 2014-01-01 00:00:00
Proceeding: TACL 2014 1
Tasks: Active Learning, Segmentation

In this paper, we study the problem of manually correcting automatic annotations of natural language in as efficient a manner as possible. We introduce a method for automatically segmenting a corpus into chunks such that many uncertain labels are grouped into the same chunk, while human supervision can be omitted altogether for other segments. A tradeoff must be found for segment sizes. Choosing short segments allows us to reduce the number of highly confident labels that are supervised by the annotator, which is useful because these labels are often already correct and supervising correct labels is a waste of effort. In contrast, long segments reduce the cognitive effort due to context switches. Our method helps find the segmentation that optimizes supervision efficiency by defining user models to predict the cost and utility of supervising each segment and solving a constrained optimization problem balancing these contradictory objectives. A user study demonstrates noticeable gains over pre-segmented, confidence-ordered baselines on two natural language processing tasks: speech transcription and word segmentation.
Dynamic Language Models for Streaming Text
Authors: Dani Yogatama, Chong Wang, Bryan R. Routledge, Noah A. Smith, Eric P. Xing
Date: 2014-01-01 00:00:00
Proceeding: TACL 2014 1
Tasks: Articles, Language Modeling, Language Modelling, Machine Translation, Topic Models

We present a probabilistic language model that captures temporal dynamics and conditions on arbitrary non-linguistic context features. These context features serve as important indicators of language changes that are otherwise difficult to capture using text data by itself. We learn our model in an efficient online fashion that is scalable for large, streaming data. With five streaming datasets from two different genres{---}economics news articles and social media{---}we evaluate our model on the task of sequential language modeling. Our model consistently outperforms competing models.
Discriminative Lexical Semantic Segmentation with Gaps: Running the MWE Gamut
Authors: Nathan Schneider, Emily Danchik, Chris Dyer, Noah A. Smith
Date: 2014-01-01 00:00:00
Proceeding: TACL 2014 1
Tasks: Chunking, Segmentation, Semantic Segmentation, Sentence

We present a novel representation, evaluation measure, and supervised models for the task of identifying the multiword expressions (MWEs) in a sentence, resulting in a lexical semantic segmentation. Our approach generalizes a standard chunking representation to encode MWEs containing gaps, thereby enabling efficient sequence tagging algorithms for feature-rich discriminative models. Experiments on a new dataset of English web text offer the first linguistically-driven evaluation of MWE identification with truly heterogeneous expression types. Our statistical sequence model greatly outperforms a lookup-based segmentation procedure, achieving nearly 60{\%} F1 for MWE identification.
Grounded Compositional Semantics for Finding and Describing Images with Sentences
Authors: Richard Socher, Andrej Karpathy, Quoc V. Le, Christopher D. Manning, Andrew Y. Ng
Date: 2014-01-01 00:00:00
Proceeding: TACL 2014 1
Tasks: Sentence

Previous work on Recursive Neural Networks (RNNs) shows that these models can produce compositional feature vectors for accurately representing and classifying sentences or images. However, the sentence vectors of previous models cannot accurately represent visually grounded meaning. We introduce the DT-RNN model which uses dependency trees to embed sentences into a vector space in order to retrieve images that are described by those sentences. Unlike previous RNN-based models which use constituency trees, DT-RNNs naturally focus on the action and agents in a sentence. They are better able to abstract from the details of word order and syntactic expression. DT-RNNs outperform other recursive and recurrent neural networks, kernelized CCA and a bag-of-words baseline on the tasks of finding an image that fits a sentence description and vice versa. They also give more similar representations to sentences that describe the same image.
Back to Basics for Monolingual Alignment: Exploiting Word Similarity and Contextual Evidence
Authors: Md. Arafat Sultan, Steven Bethard, Tamara Sumner
Date: 2014-01-01 00:00:00
Proceeding: TACL 2014 1
Tasks: Natural Language Inference, Question Answering, Text Summarization, Word Similarity

We present a simple, easy-to-replicate monolingual aligner that demonstrates state-of-the-art performance while relying on almost no supervision and a very small number of external resources. Based on the hypothesis that words with similar meanings represent potential pairs for alignment if located in similar contexts, we propose a system that operates by finding such pairs. In two intrinsic evaluations on alignment test data, our system achieves F1 scores of 88{--}92{\%}, demonstrating 1{--}3{\%} absolute improvement over the previous best system. Moreover, in two extrinsic evaluations our aligner outperforms existing aligners, and even a naive application of the aligner approaches state-of-the-art performance in each extrinsic task.
Entity Linking meets Word Sense Disambiguation: a Unified Approach
Authors: Andrea Moro, Aless Raganato, ro, Roberto Navigli
Date: 2014-01-01 00:00:00
Proceeding: TACL 2014 1
Tasks: Entity Linking, LEMMA, Reading Comprehension, Word Sense Disambiguation

Entity Linking (EL) and Word Sense Disambiguation (WSD) both address the lexical ambiguity of language. But while the two tasks are pretty similar, they differ in a fundamental respect: in EL the textual mention can be linked to a named entity which may or may not contain the exact mention, while in WSD there is a perfect match between the word form (better, its lemma) and a suitable word sense. In this paper we present Babelfy, a unified graph-based approach to EL and WSD based on a loose identification of candidate meanings coupled with a densest subgraph heuristic which selects high-coherence semantic interpretations. Our experiments show state-of-the-art performances on both tasks on 6 different datasets, including a multilingual setting. Babelfy is online at http://babelfy.org
Crosslingual and Multilingual Construction of Syntax-Based Vector Space Models
Authors: Jason Utt, Sebastian Pad{\'o}
Date: 2014-01-01 00:00:00
Proceeding: TACL 2014 1
Tasks: Translation

Syntax-based distributional models of lexical semantics provide a flexible and linguistically adequate representation of co-occurrence information. However, their construction requires large, accurately parsed corpora, which are unavailable for most languages. In this paper, we develop a number of methods to overcome this obstacle. We describe (a) a crosslingual approach that constructs a syntax-based model for a new language requiring only an English resource and a translation lexicon; and (b) multilingual approaches that combine crosslingual with monolingual information, subject to availability. We evaluate on two lexical semantic benchmarks in German and Croatian. We find that the models exhibit complementary profiles: crosslingual models yield higher accuracies while monolingual models provide better coverage. In addition, we show that simple multilingual models can successfully combine their strengths.
Entity Linking on Microblogs with Spatial and Temporal Signals
Authors: Yuan Fang, Ming-Wei Chang
Date: 2014-01-01 00:00:00
Proceeding: TACL 2014 1
Tasks: Entity Linking, Sentiment Analysis

Microblogs present an excellent opportunity for monitoring and analyzing world happenings. Given that words are often ambiguous, entity linking becomes a crucial step towards understanding microblogs. In this paper, we re-examine the problem of entity linking on microblogs. We first observe that spatiotemporal (i.e., spatial and temporal) signals play a key role, but they are not utilized in existing approaches. Thus, we propose a novel entity linking framework that incorporates spatiotemporal signals through a weakly supervised process. Using entity annotations on real-world data, our experiments show that the spatiotemporal model improves F1 by more than 10 points over existing systems. Finally, we present a qualitative study to visualize the effectiveness of our approach.
Dense Event Ordering with a Multi-Pass Architecture
Authors: Nathanael Chambers, Taylor Cassidy, Bill McDowell, Steven Bethard
Date: 2014-01-01 00:00:00
Proceeding: TACL 2014 1
Tasks: Relation Classification, Temporal Information Extraction

The past 10 years of event ordering research has focused on learning partial orderings over document events and time expressions. The most popular corpus, the TimeBank, contains a small subset of the possible ordering graph. Many evaluations follow suit by only testing certain pairs of events (e.g., only main verbs of neighboring sentences). This has led most research to focus on specific learners for partial labelings. This paper attempts to nudge the discussion from identifying some relations to all relations. We present new experiments on strongly connected event graphs that contain âˆ¼10 times more relations per document than the TimeBank. We also describe a shift away from the single learner to a sieve-based architecture that naturally blends multiple learners into a precision-ranked cascade of sieves. Each sieve adds labels to the event graph one at a time, and earlier sieves inform later ones through transitive closure. This paper thus describes innovations in both approach and task. We experiment on the densest event graphs to date and show a 14{\%} gain over state-of-the-art.
Multi-Modal Models for Concrete and Abstract Concept Meaning
Authors: Felix Hill, Roi Reichart, Anna Korhonen
Date: 2014-01-01 00:00:00
Proceeding: TACL 2014 1
Tasks: Language Acquisition

Multi-modal models that learn semantic representations from both linguistic and perceptual input outperform language-only models on a range of evaluations, and better reflect human concept acquisition. Most perceptual input to such models corresponds to concrete noun concepts and the superiority of the multi-modal approach has only been established when evaluating on such concepts. We therefore investigate which concepts can be effectively learned by multi-modal models. We show that concreteness determines both which linguistic features are most informative and the impact of perceptual input in such models. We then introduce ridge regression as a means of propagating perceptual information from concrete nouns to more abstract concepts that is more robust than previous approaches. Finally, we present weighted gram matrix combination, a means of combining representations from distinct modalities that outperforms alternatives when both modalities are sufficiently rich.
The Benefits of a Model of Annotation
Authors: Rebecca J. Passonneau, Bob Carpenter
Date: 2014-01-01 00:00:00
Proceeding: TACL 2014 1
Tasks: Epidemiology, model

Standard agreement measures for interannotator reliability are neither necessary nor sufficient to ensure a high quality corpus. In a case study of word sense annotation, conventional methods for evaluating labels from trained annotators are contrasted with a probabilistic annotation model applied to crowdsourced data. The annotation model provides far more information, including a certainty measure for each gold standard label; the crowdsourced data was collected at less than half the cost of the conventional approach.