bluuebunny/crossref_metadata_2025 huggingface dataset:
Generating Training Data for Semantic Role Labeling based on Label Transfer from Linked Lexical Resources
Authors: Hartmann Silvana , Eckle-Kohler Judith , Gurevych Iryna
Published: December 2018

We present a new approach for generating role-labeled training data using Linked Lexical Resources, i.e., integrated lexical resources that combine several resources (e.g., Word-Net, FrameNet, Wiktionary) by linking them on the sense or on the role level. Unlike resource-based supervision in relation extraction, we focus on complex linguistic annotations, more specifically FrameNet senses and roles. The automatically labeled training data ( www.ukp.tu-darmstadt.de/knowledge-based-srl/ ) are evaluated on four corpora from different domains for the tasks of word sense disambiguation and semantic role classification. Results show that classifiers trained on our generated data equal those resulting from a standard supervised setting.
Deep Recurrent Models with Fast-Forward Connections for Neural Machine Translation
Authors: Zhou Jie , Cao Ying , Wang Xuguang , Li Peng , Xu Wei
Published: December 2018

Neural machine translation (NMT) aims at solving machine translation (MT) problems using neural networks and has exhibited promising results in recent years. However, most of the existing NMT models are shallow and there is still a performance gap between a single NMT model and the best conventional MT system. In this work, we introduce a new type of linear connections, named fast-forward connections, based on deep Long Short-Term Memory (LSTM) networks, and an interleaved bi-directional architecture for stacking the LSTM layers. Fast-forward connections play an essential role in propagating the gradients and building a deep topology of depth 16. On the WMT’14 English-to-French task, we achieve BLEU=37.7 with a single attention model, which outperforms the corresponding single shallow model by 6.2 BLEU points. This is the first time that a single NMT model achieves state-of-the-art performance and outperforms the best conventional model by 0.7 BLEU points. We can still achieve BLEU=36.3 even without using an attention mechanism. After special handling of unknown words and model ensembling, we obtain the best score reported to date on this task with BLEU=40.4. Our models are also validated on the more difficult WMT’14 English-to-German task.
Easy-First Dependency Parsing with Hierarchical Tree LSTMs
Authors: Kiperwasser Eliyahu , Goldberg Yoav
Published: December 2018

We suggest a compositional vector representation of parse trees that relies on a recursive combination of recurrent-neural network encoders. To demonstrate its effectiveness, we use the representation as the backbone of a greedy, bottom-up dependency parser, achieving very strong accuracies for English and Chinese, without relying on external word embeddings. The parser’s implementation is available for download at the first author’s webpage.
Minimally Supervised Number Normalization
Authors: Gorman Kyle , Sproat Richard
Published: December 2018

We propose two models for verbalizing numbers, a key component in speech recognition and synthesis systems. The first model uses an end-to-end recurrent neural network. The second model, drawing inspiration from the linguistics literature, uses finite-state transducers constructed with a minimal amount of training data. While both models achieve near-perfect performance, the latter model can be trained using several orders of magnitude less data than the former, making it particularly useful for low-resource languages.
Reasoning about Quantities in Natural Language
Authors: Roy Subhro , Vieira Tim , Roth Dan
Published: December 2018

Little work from the Natural Language Processing community has targeted the role of quantities in Natural Language Understanding. This paper takes some key steps towards facilitating reasoning about quantities expressed in natural language. We investigate two different tasks of numerical reasoning. First, we consider Quantity Entailment, a new task formulated to understand the role of quantities in general textual inference tasks. Second, we consider the problem of automatically understanding and solving elementary school math word problems. In order to address these quantitative reasoning problems we first develop a computational approach which we show to successfully recognize and normalize textual expressions of quantities. We then use these capabilities to further develop algorithms to assist reasoning in the context of the aforementioned tasks.
Sprite: Generalizing Topic Models with Structured Priors
Authors: Paul Michael J. , Dredze Mark
Published: December 2018

We introduce Sprite, a family of topic models that incorporates structure into model priors as a function of underlying components. The structured priors can be constrained to model topic hierarchies, factorizations, correlations, and supervision, allowing Sprite to be tailored to particular settings. We demonstrate this flexibility by constructing a Sprite-based model to jointly infer topic hierarchies and author perspective, which we apply to corpora of political debates and online reviews. We show that the model learns intuitive topics, outperforming several other topic models at predictive tasks.
Gappy Pattern Matching on GPUs for On-Demand Extraction of Hierarchical Translation Grammars
Authors: He Hua , Lin Jimmy , Lopez Adam
Published: December 2018

Grammars for machine translation can be materialized on demand by finding source phrases in an indexed parallel corpus and extracting their translations. This approach is limited in practical applications by the computational expense of online lookup and extraction. For phrase-based models, recent work has shown that on-demand grammar extraction can be greatly accelerated by parallelization on general purpose graphics processing units (GPUs), but these algorithms do not work for hierarchical models, which require matching patterns that contain gaps. We address this limitation by presenting a novel GPU algorithm for on-demand hierarchical grammar extraction that is at least an order of magnitude faster than a comparable CPU algorithm when processing large batches of sentences. In terms of end-to-end translation, with decoding on the CPU, we increase throughput by roughly two thirds on a standard MT evaluation dataset. The GPU necessary to achieve these improvements increases the cost of a server by about a third. We believe that GPU-based extraction of hierarchical grammars is an attractive proposition, particularly for MT applications that demand high throughput.
Unsupervised Declarative Knowledge Induction for Constraint-Based Learning of Information Structure in Scientific Documents
Authors: Guo Yufan , Reichart Roi , Korhonen Anna
Published: December 2018

Inferring the information structure of scientific documents is useful for many NLP applications. Existing approaches to this task require substantial human effort. We propose a framework for constraint learning that reduces human involvement considerably. Our model uses topic models to identify latent topics and their key linguistic features in input documents, induces constraints from this information and maps sentences to their dominant information structure categories through a constrained unsupervised model. When the induced constraints are combined with a fully unsupervised model, the resulting model challenges existing lightly supervised feature-based models as well as unsupervised models that use manually constructed declarative knowledge. Our results demonstrate that useful declarative knowledge can be learned from data with very limited human involvement.
From Visual Attributes to Adjectives through Decompositional Distributional Semantics
Authors: Lazaridou Angeliki , Dinu Georgiana , Liska Adam , Baroni Marco
Published: December 2018

As automated image analysis progresses, there is increasing interest in richer linguistic annotation of pictures, with attributes of objects (e.g., furry, brown…) attracting most attention. By building on the recent “zero-shot learning” approach, and paying attention to the linguistic nature of attributes as noun modifiers, and specifically adjectives, we show that it is possible to tag images with attribute-denoting adjectives even when no training data containing the relevant annotation are available. Our approach relies on two key observations. First, objects can be seen as bundles of attributes, typically expressed as adjectival modifiers (a dog is something furry, brown, etc.), and thus a function trained to map visual representations of objects to nominal labels can implicitly learn to map attributes to adjectives. Second, objects and attributes come together in pictures (the same thing is a dog and it is brown). We can thus achieve better attribute (and object) label retrieval by treating images as “visual phrases”, and decomposing their linguistic representation into an attribute-denoting adjective and an object-denoting noun. Our approach performs comparably to a method exploiting manual attribute annotation, it out-performs various competitive alternatives in both attribute and object annotation, and it automatically constructs attribute-centric representations that significantly improve performance in supervised object recognition.
A Graph-based Lattice Dependency Parser for Joint Morphological Segmentation and Syntactic Analysis
Authors: Seeker Wolfgang , Çetinoğlu Özlem
Published: December 2018

Space-delimited words in Turkish and Hebrew text can be further segmented into meaningful units, but syntactic and semantic context is necessary to predict segmentation. At the same time, predicting correct syntactic structures relies on correct segmentation. We present a graph-based lattice dependency parser that operates on morphological lattices to represent different segmentations and morphological analyses for a given input sentence. The lattice parser predicts a dependency tree over a path in the lattice and thus solves the joint task of segmentation, morphological analysis, and syntactic parsing. We conduct experiments on the Turkish and the Hebrew treebank and show that the joint model outperforms three state-of-the-art pipeline systems on both data sets. Our work corroborates findings from constituency lattice parsing for Hebrew and presents the first results for full lattice parsing on Turkish.
Latent Structures for Coreference Resolution
Authors: Martschat Sebastian , Strube Michael
Published: December 2018

Machine learning approaches to coreference resolution vary greatly in the modeling of the problem: while early approaches operated on the mention pair level, current research focuses on ranking architectures and antecedent trees. We propose a unified representation of different approaches to coreference resolution in terms of the structure they operate on. We represent several coreference resolution approaches proposed in the literature in our framework and evaluate their performance. Finally, we conduct a systematic analysis of the output of these approaches, highlighting differences and similarities.
Modeling Word Forms Using Latent Underlying Morphs and Phonology
Authors: Cotterell Ryan , Peng Nanyun , Eisner Jason
Published: December 2018

The observed pronunciations or spellings of words are often explained as arising from the “underlying forms” of their morphemes. These forms are latent strings that linguists try to reconstruct by hand. We propose to reconstruct them automatically at scale, enabling generalization to new words. Given some surface word types of a concatenative language along with the abstract morpheme sequences that they express, we show how to recover consistent underlying forms for these morphemes, together with the (stochastic) phonology that maps each concatenation of underlying forms to a surface form. Our technique involves loopy belief propagation in a natural directed graphical model whose variables are unknown strings and whose conditional distributions are encoded as finite-state machines with trainable weights. We define training and evaluation paradigms for the task of surface word prediction, and report results on subsets of 7 languages.
Context-aware Frame-Semantic Role Labeling
Authors: Roth Michael , Lapata Mirella
Published: December 2018

Frame semantic representations have been useful in several applications ranging from text-to-scene generation, to question answering and social network analysis. Predicting such representations from raw text is, however, a challenging task and corresponding models are typically only trained on a small set of sentence-level annotations. In this paper, we present a semantic role labeling system that takes into account sentence and discourse context. We introduce several new features which we motivate based on linguistic insights and experimentally demonstrate that they lead to significant improvements over the current state-of-the-art in FrameNet-based semantic role labeling.
Learning Structural Kernels for Natural Language Processing
Authors: Beck Daniel , Cohn Trevor , Hardmeier Christian , Specia Lucia
Published: December 2018

Structural kernels are a flexible learning paradigm that has been widely used in Natural Language Processing. However, the problem of model selection in kernel-based methods is usually overlooked. Previous approaches mostly rely on setting default values for kernel hyperparameters or using grid search, which is slow and coarse-grained. In contrast, Bayesian methods allow efficient model selection by maximizing the evidence on the training data through gradient-based methods. In this paper we show how to perform this in the context of structural kernels by using Gaussian Processes. Experimental results on tree kernels show that this procedure results in better prediction performance compared to hyperparameter optimization via grid search. The framework proposed in this paper can be adapted to other structures besides trees, e.g., strings and graphs, thereby extending the utility of kernel-based methods.
Semantic Proto-Roles
Authors: Reisinger Drew , Rudinger Rachel , Ferraro Francis , Harman Craig , Rawlins Kyle , Van Durme Benjamin
Published: December 2018

We present the first large-scale, corpus based verification of Dowty’s seminal theory of proto-roles. Our results demonstrate both the need for and the feasibility of a property-based annotation scheme of semantic relationships, as opposed to the currently dominant notion of categorical roles.
Approximation-Aware Dependency Parsing by Belief Propagation
Authors: Gormley Matthew R. , Dredze Mark , Eisner Jason
Published: December 2018

We show how to train the fast dependency parser of Smith and Eisner (2008) for improved accuracy. This parser can consider higher-order interactions among edges while retaining O( n3) runtime. It outputs the parse with maximum expected recall—but for speed, this expectation is taken under a posterior distribution that is constructed only approximately, using loopy belief propagation through structured factors. We show how to adjust the model parameters to compensate for the errors introduced by this approximation, by following the gradient of the actual loss on training data. We find this gradient by back-propagation. That is, we treat the entire parser (approximations and all) as a differentiable circuit, as others have done for loopy CRFs (Domke, 2010; Stoyanov et al., 2011; Domke, 2011; Stoyanov and Eisner, 2012). The resulting parser obtains higher accuracy with fewer iterations of belief propagation than one trained by conditional log-likelihood.
Imitation Learning of Agenda-based Semantic Parsers
Authors: Berant Jonathan , Liang Percy
Published: December 2018

Semantic parsers conventionally construct logical forms bottom-up in a fixed order, resulting in the generation of many extraneous partial logical forms. In this paper, we combine ideas from imitation learning and agenda-based parsing to train a semantic parser that searches partial logical forms in a more strategic order. Empirically, our parser reduces the number of constructed partial logical forms by an order of magnitude, and obtains a 6x-9x speedup over fixed-order parsing, while maintaining comparable accuracy.
Heterogeneous Networks and Their Applications: Scientometrics, Name Disambiguation, and Topic Modeling
Authors: King Ben , Jha Rahul , Radev Dragomir R.
Published: December 2018

We present heterogeneous networks as a way to unify lexical networks with relational data. We build a unified ACL Anthology network, tying together the citation, author collaboration, and term-cooccurence networks with affiliation and venue relations. This representation proves to be convenient and allows problems such as name disambiguation, topic modeling, and the measurement of scientific impact to be easily solved using only this network and off-the-shelf graph algorithms.
FLORS: Fast and Simple Domain Adaptation for Part-of-Speech Tagging
Authors: Schnabel Tobias , Schütze Hinrich
Published: December 2018

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: Lui Marco , Lau Jey Han , Baldwin Timothy
Published: December 2018

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: Pitler Emily
Published: December 2018

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: Wang Mengqiu , Manning Christopher D.
Published: December 2018

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: Young Peter , Lai Alice , Hodosh Micah , Hockenmaier Julia
Published: December 2018

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.
Joint Incremental Disfluency Detection and Dependency Parsing
Authors: Honnibal Matthew , Johnson Mark
Published: December 2018

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: Styler William F. , Bethard Steven , Finan Sean , Palmer Martha , Pradhan Sameer , de Groen Piet C , Erickson Brad , Miller Timothy , Lin Chen , Savova Guergana , Pustejovsky James
Published: December 2018

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.
Discriminative Lexical Semantic Segmentation with Gaps: Running the MWE Gamut
Authors: Schneider Nathan , Danchik Emily , Dyer Chris , Smith Noah A.
Published: December 2018

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.
Back to Basics for Monolingual Alignment: Exploiting Word Similarity and Contextual Evidence
Authors: Sultan Md Arafat , Bethard Steven , Sumner Tamara
Published: December 2018

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.
Dense Event Ordering with a Multi-Pass Architecture
Authors: Chambers Nathanael , Cassidy Taylor , McDowell Bill , Bethard Steven
Published: December 2018

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 ∼10 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.
Improved CCG Parsing with Semi-supervised Supertagging
Authors: Lewis Mike , Steedman Mark
Published: December 2018

Current supervised parsers are limited by the size of their labelled training data, making improving them with unlabelled data an important goal. We show how a state-of-the-art CCG parser can be enhanced, by predicting lexical categories using unsupervised vector-space embeddings of words. The use of word embeddings enables our model to better generalize from the labelled data, and allows us to accurately assign lexical categories without depending on a POS-tagger. Our approach leads to substantial improvements in dependency parsing results over the standard supervised CCG parser when evaluated on Wall Street Journal (0.8%), Wikipedia (1.8%) and biomedical (3.4%) text. We compare the performance of two recently proposed approaches for classification using a wide variety of word embeddings. We also give a detailed error analysis demonstrating where using embeddings outperforms traditional feature sets, and showing how including POS features can decrease accuracy.
2-Slave Dual Decomposition for Generalized Higher Order CRFs
Authors: Qian Xian , Liu Yang
Published: December 2018

We show that the decoding problem in generalized Higher Order Conditional Random Fields (CRFs) can be decomposed into two parts: one is a tree labeling problem that can be solved in linear time using dynamic programming; the other is a supermodular quadratic pseudo-Boolean maximization problem, which can be solved in cubic time using a minimum cut algorithm. We use dual decomposition to force their agreement. Experimental results on Twitter named entity recognition and sentence dependency tagging tasks show that our method outperforms spanning tree based dual decomposition.
TreeTalk: Composition and Compression of Trees for Image Descriptions
Authors: Kuznetsova Polina , Ordonez Vicente , Berg Tamara L. , Choi Yejin
Published: December 2018

We present a new tree based approach to composing expressive image descriptions that makes use of naturally occuring web images with captions. We investigate two related tasks: image caption generalization and generation, where the former is an optional subtask of the latter. The high-level idea of our approach is to harvest expressive phrases (as tree fragments) from existing image descriptions, then to compose a new description by selectively combining the extracted (and optionally pruned) tree fragments. Key algorithmic components are tree composition and compression, both integrating tree structure with sequence structure. Our proposed system attains significantly better performance than previous approaches for both image caption generalization and generation. In addition, our work is the first to show the empirical benefit of automatically generalized captions for composing natural image descriptions.
Unsupervised Discovery of Biographical Structure from Text
Authors: Bamman David , Smith Noah A.
Published: December 2018

We present a method for discovering abstract event classes in biographies, based on a probabilistic latent-variable model. Taking as input timestamped text, we exploit latent correlations among events to learn a set of event classes (such as Born, Graduates High School, and Becomes Citizen), along with the typical times in a person’s life when those events occur. In a quantitative evaluation at the task of predicting a person’s age for a given event, we find that our generative model outperforms a strong linear regression baseline, along with simpler variants of the model that ablate some features. The abstract event classes that we learn allow us to perform a large-scale analysis of 242,970 Wikipedia biographies. Though it is known that women are greatly underrepresented on Wikipedia—not only as editors (Wikipedia, 2011) but also as subjects of articles (Reagle and Rhue, 2011)—we find that there is a bias in their characterization as well, with biographies of women containing significantly more emphasis on events of marriage and divorce than biographies of men.
A New Parsing Algorithm for Combinatory Categorial Grammar
Authors: Kuhlmann Marco , Satta Giorgio
Published: December 2018

We present a polynomial-time parsing algorithm for CCG, based on a new decomposition of derivations into small, shareable parts. Our algorithm has the same asymptotic complexity, O( n6), as a previous algorithm by Vijay-Shanker and Weir (1993), but is easier to understand, implement, and prove correct.
Building a State-of-the-Art Grammatical Error Correction System
Authors: Rozovskaya Alla , Roth Dan
Published: December 2018

This paper identifies and examines the key principles underlying building a state-of-the-art grammatical error correction system. We do this by analyzing the Illinois system that placed first among seventeen teams in the recent CoNLL-2013 shared task on grammatical error correction.

The system focuses on five different types of errors common among non-native English writers. We describe four design principles that are relevant for correcting all of these errors, analyze the system along these dimensions, and show how each of these dimensions contributes to the performance.
Extracting Lexically Divergent Paraphrases from Twitter
Authors: Xu Wei , Ritter Alan , Callison-Burch Chris , Dolan William B. , Ji Yangfeng
Published: December 2018

We present MultiP (Multi-instance Learning Paraphrase Model), a new model suited to identify paraphrases within the short messages on Twitter. We jointly model paraphrase relations between word and sentence pairs and assume only sentence-level annotations during learning. Using this principled latent variable model alone, we achieve the performance competitive with a state-of-the-art method which combines a latent space model with a feature-based supervised classifier. Our model also captures lexically divergent paraphrases that differ from yet complement previous methods; combining our model with previous work significantly outperforms the state-of-the-art. In addition, we present a novel annotation methodology that has allowed us to crowdsource a paraphrase corpus from Twitter. We make this new dataset available to the research community.
Online Adaptor Grammars with Hybrid Inference
Authors: Zhai Ke , Boyd-Graber Jordan , Cohen Shay B.
Published: December 2018

Adaptor grammars are a flexible, powerful formalism for defining nonparametric, unsupervised models of grammar productions. This flexibility comes at the cost of expensive inference. We address the difficulty of inference through an online algorithm which uses a hybrid of Markov chain Monte Carlo and variational inference. We show that this inference strategy improves scalability without sacrificing performance on unsupervised word segmentation and topic modeling tasks.
Erratum: “The Benefits of a Model of Annotation”
Authors: Passonneau Rebecca J. , Carpenter Bob
Published: December 2018

Correction for the URL in footnote 7.
Token and Type Constraints for Cross-Lingual Part-of-Speech Tagging
Authors: Täckström Oscar , Das Dipanjan , Petrov Slav , McDonald Ryan , Nivre Joakim
Published: December 2018

We consider the construction of part-of-speech taggers for resource-poor languages. Recently, manually constructed tag dictionaries from Wiktionary and dictionaries projected via bitext have been used as type constraints to overcome the scarcity of annotated data in this setting. In this paper, we show that additional token constraints can be projected from a resource-rich source language to a resource-poor target language via word-aligned bitext. We present several models to this end; in particular a partially observed conditional random field model, where coupled token and type constraints provide a partial signal for training. Averaged across eight previously studied Indo-European languages, our model achieves a 25% relative error reduction over the prior state of the art. We further present successful results on seven additional languages from different families, empirically demonstrating the applicability of coupled token and type constraints across a diverse set of languages.
Finding Optimal 1-Endpoint-Crossing Trees
Authors: Pitler Emily , Kannan Sampath , Marcus Mitchell
Published: December 2018

Dependency parsing algorithms capable of producing the types of crossing dependencies seen in natural language sentences have traditionally been orders of magnitude slower than algorithms for projective trees. For 95.8–99.8% of dependency parses in various natural language treebanks, whenever an edge is crossed, the edges that cross it all have a common vertex. The optimal dependency tree that satisfies this 1-Endpoint-Crossing property can be found with an O( n4) parsing algorithm that recursively combines forests over intervals with one exterior point. 1-Endpoint-Crossing trees also have natural connections to linguistics and another class of graphs that has been studied in NLP.
Using Pivot-Based Paraphrasing and Sentiment Profiles to Improve a Subjectivity Lexicon for Essay Data
Authors: Klebanov Beata Beigman , Madnani Nitin , Burstein Jill
Published: December 2018

We demonstrate a method of improving a seed sentiment lexicon developed on essay data by using a pivot-based paraphrasing system for lexical expansion coupled with sentiment profile enrichment using crowdsourcing. Profile enrichment alone yields up to 15% improvement in the accuracy of the seed lexicon on 3-way sentence-level sentiment polarity classification of essay data. Using lexical expansion in addition to sentiment profiles provides a further 7% improvement in performance. Additional experiments show that the proposed method is also effective with other subjectivity lexicons and in a different domain of application (product reviews).
Efficient Stacked Dependency Parsing by Forest Reranking
Authors: Hayashi Katsuhiko , Kondo Shuhei , Matsumoto Yuji
Published: December 2018

This paper proposes a discriminative forest reranking algorithm for dependency parsing that can be seen as a form of efficient stacked parsing. A dynamic programming shift-reduce parser produces a packed derivation forest which is then scored by a discriminative reranker, using the 1-best tree output by the shift-reduce parser as guide features in addition to third-order graph-based features. To improve efficiency and accuracy, this paper also proposes a novel shift-reduce parser that eliminates the spurious ambiguity of arc-standard transition systems. Testing on the English Penn Treebank data, forest reranking gave a state-of-the-art unlabeled dependency accuracy of 93.12.
Dijkstra-WSA: A Graph-Based Approach to Word Sense Alignment
Authors: Matuschek Michael , Gurevych Iryna
Published: December 2018

In this paper, we present Dijkstra-WSA, a novel graph-based algorithm for word sense alignment. We evaluate it on four different pairs of lexical-semantic resources with different characteristics (WordNet-OmegaWiki, WordNet-Wiktionary, GermaNet-Wiktionary and WordNet-Wikipedia) and show that it achieves competitive performance on 3 out of 4 datasets. Dijkstra-WSA outperforms the state of the art on every dataset if it is combined with a back-off based on gloss similarity. We also demonstrate that Dijkstra-WSA is not only flexibly applicable to different resources but also highly parameterizable to optimize for precision or recall.
Joint Arc-factored Parsing of Syntactic and Semantic Dependencies
Authors: Lluís Xavier , Carreras Xavier , Màrquez Lluís
Published: December 2018

In this paper we introduce a joint arc-factored model for syntactic and semantic dependency parsing. The semantic role labeler predicts the full syntactic paths that connect predicates with their arguments. This process is framed as a linear assignment task, which allows to control some well-formedness constraints. For the syntactic part, we define a standard arc-factored dependency model that predicts the full syntactic tree. Finally, we employ dual decomposition techniques to produce consistent syntactic and predicate-argument structures while searching over a large space of syntactic configurations. In experiments on the CoNLL-2009 English benchmark we observe very competitive results.
Minimally-Supervised Morphological Segmentation using Adaptor Grammars
Authors: Sirts Kairit , Goldwater Sharon
Published: December 2018

This paper explores the use of Adaptor Grammars, a nonparametric Bayesian modelling framework, for minimally supervised morphological segmentation. We compare three training methods: unsupervised training, semi-supervised training, and a novel model selection method. In the model selection method, we train unsupervised Adaptor Grammars using an over-articulated metagrammar, then use a small labelled data set to select which potential morph boundaries identified by the metagrammar should be returned in the final output. We evaluate on five languages and show that semi-supervised training provides a boost over unsupervised training, while the model selection method yields the best average results over all languages and is competitive with state-of-the-art semi-supervised systems. Moreover, this method provides the potential to tune performance according to different evaluation metrics or downstream tasks.
Large-scale Word Alignment Using Soft Dependency Cohesion Constraints
Authors: Wang Zhiguo , Zong Chengqing
Published: December 2018

Dependency cohesion refers to the observation that phrases dominated by disjoint dependency subtrees in the source language generally do not overlap in the target language. It has been verified to be a useful constraint for word alignment. However, previous work either treats this as a hard constraint or uses it as a feature in discriminative models, which is ineffective for large-scale tasks. In this paper, we take dependency cohesion as a soft constraint, and integrate it into a generative model for large-scale word alignment experiments. We also propose an approximate EM algorithm and a Gibbs sampling algorithm to estimate model parameters in an unsupervised manner. Experiments on large-scale Chinese-English translation tasks demonstrate that our model achieves improvements in both alignment quality and translation quality.
Distributional Semantics Beyond Words: Supervised Learning of Analogy and Paraphrase
Authors: Turney Peter D.
Published: December 2018

There have been several efforts to extend distributional semantics beyond individual words, to measure the similarity of word pairs, phrases, and sentences (briefly, tuples; ordered sets of words, contiguous or noncontiguous). One way to extend beyond words is to compare two tuples using a function that combines pairwise similarities between the component words in the tuples. A strength of this approach is that it works with both relational similarity (analogy) and compositional similarity (paraphrase). However, past work required hand-coding the combination function for different tasks. The main contribution of this paper is that combination functions are generated by supervised learning. We achieve state-of-the-art results in measuring relational similarity between word pairs (SAT analogies and SemEval 2012 Task 2) and measuring compositional similarity between noun-modifier phrases and unigrams (multiple-choice paraphrase questions).
Data-Driven Metaphor Recognition and Explanation
Authors: Li Hongsong , Zhu Kenny Q. , Wang Haixun
Published: December 2018

Recognizing metaphors and identifying the source-target mappings is an important task as metaphorical text poses a big challenge for machine reading. To address this problem, we automatically acquire a metaphor knowledge base and an isA knowledge base from billions of web pages. Using the knowledge bases, we develop an inference mechanism to recognize and explain the metaphors in the text. To our knowledge, this is the first purely data-driven approach of probabilistic metaphor acquisition, recognition, and explanation. Our results shows that it significantly outperforms other state-of-the-art methods in recognizing and explaining metaphors.
Joint Morphological and Syntactic Analysis for Richly Inflected Languages
Authors: Bohnet Bernd , Nivre Joakim , Boguslavsky Igor , Farkas Richárd , Ginter Filip , Hajič Jan
Published: December 2018

Joint morphological and syntactic analysis has been proposed as a way of improving parsing accuracy for richly inflected languages. Starting from a transition-based model for joint part-of-speech tagging and dependency parsing, we explore different ways of integrating morphological features into the model. We also investigate the use of rule-based morphological analyzers to provide hard or soft lexical constraints and the use of word clusters to tackle the sparsity of lexical features. Evaluation on five morphologically rich languages (Czech, Finnish, German, Hungarian, and Russian) shows consistent improvements in both morphological and syntactic accuracy for joint prediction over a pipeline model, with further improvements thanks to lexical constraints and word clusters. The final results improve the state of the art in dependency parsing for all languages.
Preparation of small-sized 3D amber samples: state of the technique

Authors: SIDORCHUK EKATERINA A. , VORONTSOV DMITRY D.
Published: December 2018

Preparation of a piece of amber is an inevitable step for any paleontologist working with this precious medium. To see the life-like detail of a biological inclusion, the fossil resin itself has to be trimmed to the minimum, then surfaces polished flat for viewing of the fossil, preferably from all sides. This concerns inclusions of any size, but the smallest—mites, collembolans, scale insects and others—are the most challenging to the abilities of the human hand. Making a millimeter-large cube of a brittle material containing an unscathed tiny fossil was feasible for only a few artists of amber preparation some ten years ago. Now, with tools and techniques having evolved since, the task is feasible for any attentive preparator after a few days of training. This paper is an account of our progress.
First Dicranomyia Stephens, 1829 (Diptera, Limoniidae) in Colombian copal

Authors: KRZEMIŃSKI WIESŁAW , KANIA IWONA , KOPEĆ KATARZYNA , OKOŃ MAGDALENA
Published: December 2018

The description of a new species of the genus Dicranomyia Stephens, 1829 (Diptera: Limoniidae) from Colombian copal is presented. The newly described species Dicranomyia (Dicranomyia) colombiana sp. nov. is the first representative of the genus in Colombian copal. The ecological and biogeographical patterns, morphological evolution and the aspects evolutionary history of extinct and extant representatives of Dicranomyia (with particular references to the representatives in Colombian copal) are discussed.
Scapania marsupelloides sp. nov. (Scapaniaceae, Marchantiophyta), a remarkable new species near the base of Scapania phylogeny
Authors: MAMONTOV YURIY S. , VILNET ANNA A. , POTEMKIN ALEXEY D.
Published: December 2018

Scapania marsupelloides is described as new to science based on collection from Yunnan, China. The species looks different from the other known Scapania species and similar to Marsupella sphacelata. Molecular phylogenic analyses of ITS1-2 and trnL–trnF sequences suggested the close affinity of Scapania marsupelloides to S. contorta of the subgenus Ascapania. Description, line drawings, color and SEM images, and keys for differentiation of S. marsupelloides from S. cuspiduligera, S. contorta, S. pseudocontorta, S. hians and Marsupella sphacelata are provided.
An account of the liverwort genus Porella in Thailand with a new record, P. obtusata var. macroloba and the occurrence of asymmetrical underleaves associated with left-right symmetry in the genus
Authors: SUKKHARAK PHIANGPHAK , CHANTANAORRAPINT SAHUT
Published: December 2018

An updated checklist of twelve taxa of Porella in Thailand is presented, based on literature and some recent collections. Of these, P. obtusata var. macroloba is reported new for Thailand. The variety possesses asymmetrical underleaves associated with left-right symmetry, the first report of this character in the genus Porella. A description and illustration of P. obtusata var. macroloba are provided.
Ontogenetic development and redescription of Eotetranychus kankitus (Acariformes: Tetranychidae)
Authors: LI JUAN , GUO JIAN-JUN , JIN DAO-CHAO
Published: December 2018

Eotetranychus kankitus Ehara, 1955 is redescribed from females, males, deutonymphs, protonymphs and larvae collected on Citrus reticulata Blanco (Rutaceae) from China. The ontogenetic changes in leg chaetotaxy are provided and compared with the similar species Eotetranychus tiliarium (Hermann, 1804) and the considerably hypotrichous species Eotetranychus xuzhouensis (Wang & Ma, 1987).
Description of a new species of Prolixus (Acari: Trombidiformes: Tenuipalpidae) from Austroderia splendens (Poaceae) in New Zealand, with discussion of its ontogenetic patterns in chaetotaxy
Authors: XU YUN , ZHANG FEI-PING , ZHANG ZHI-QIANG
Published: December 2018

A new species, Prolixus splendens sp. nov., collected from leaves of the grass Austroderia splendens (Poaceae) in Auckland, New Zealand, is described and illustrated. Only five species of Prolixus have previously been reported, and all were recorded from the genus Gahnia (Cyperaceae). In this paper, we present the ontogenetic additions in idiosomal and leg chaetotaxy from larva to adult for the new species. A key to world species of Prolixus is also proposed.
Two new species of Tenuipalpus sensu stricto (Acari: Tenuipalpidae) from Brazil, with a discussion on the ontogeny of leg setae
Authors: CASTRO ELIZEU B. , BEARD JENNIFER J. , OCHOA RONALD , BAUCHAN GARY R. , FERES REINALDO J.F.
Published: December 2018

The Amazon and the Cerrado are the two largest biomes in Brazil, and combined represent nearly 70% of the natural vegetation in the country. Recent surveys of the mite fauna present in these two biomes have revealed a great number of new species. In this paper, we describe the adult females, deutonymphs, protonymphs, larvae and eggs of two new species of Tenuipalpidae (Trombidiformes: Tetranychoidea), Tenuipalpus protium sp. nov., collected on Protium heptaphyllum (Burseraceae), and Tenuipalpus kitajimai sp. nov., collected on an unidentified plant of the family Sapindaceae, from the Cerrado and the Amazon Rainforest, respectively. Females of these two new species bear two distinct cuticular crests on the dorsal opisthosoma, one transverse crest anteriorly and one longitudinal crest mesally. The ontogenetic changes in the idiosomal and leg chaetotaxy of all stages are presented for both species. Adult males are not known for either species.