bluuebunny/crossref_metadata_2025 huggingface dataset:
Word Embeddings as Metric Recovery in Semantic Spaces
Authors: Hashimoto Tatsunori B. , Alvarez-Melis David , Jaakkola Tommi S.
Published: December 2018

Continuous word representations have been remarkably useful across NLP tasks but remain poorly understood. We ground word embeddings in semantic spaces studied in the cognitive-psychometric literature, taking these spaces as the primary objects to recover. To this end, we relate log co-occurrences of words in large corpora to semantic similarity assessments and show that co-occurrences are indeed consistent with an Euclidean semantic space hypothesis. Framing word embedding as metric recovery of a semantic space unifies existing word embedding algorithms, ties them to manifold learning, and demonstrates that existing algorithms are consistent metric recovery methods given co-occurrence counts from random walks. Furthermore, we propose a simple, principled, direct metric recovery algorithm that performs on par with the state-of-the-art word embedding and manifold learning methods. Finally, we complement recent focus on analogies by constructing two new inductive reasoning datasets—series completion and classification—and demonstrate that word embeddings can be used to solve them as well.
Comparing Apples to Apple: The Effects of Stemmers on Topic Models
Authors: Schofield Alexandra , Mimno David
Published: December 2018

Rule-based stemmers such as the Porter stemmer are frequently used to preprocess English corpora for topic modeling. In this work, we train and evaluate topic models on a variety of corpora using several different stemming algorithms. We examine several different quantitative measures of the resulting models, including likelihood, coherence, model stability, and entropy. Despite their frequent use in topic modeling, we find that stemmers produce no meaningful improvement in likelihood and coherence and in fact can degrade topic stability.
Sparse Non-negative Matrix Language Modeling
Authors: Pelemans Joris , Shazeer Noam , Chelba Ciprian
Published: December 2018

We present Sparse Non-negative Matrix (SNM) estimation, a novel probability estimation technique for language modeling that can efficiently incorporate arbitrary features. We evaluate SNM language models on two corpora: the One Billion Word Benchmark and a subset of the LDC English Gigaword corpus. Results show that SNM language models trained with n-gram features are a close match for the well-established Kneser-Ney models. The addition of skip-gram features yields a model that is in the same league as the state-of-the-art recurrent neural network language models, as well as complementary: combining the two modeling techniques yields the best known result on the One Billion Word Benchmark. On the Gigaword corpus further improvements are observed using features that cross sentence boundaries. The computational advantages of SNM estimation over both maximum entropy and neural network estimation are probably its main strength, promising an approach that has large flexibility in combining arbitrary features and yet scales gracefully to large amounts of data.
Multi-lingual Dependency Parsing Evaluation: a Large-scale Analysis of Word Order Properties using Artificial Data
Authors: Gulordava Kristina , Merlo Paola
Published: December 2018

The growing work in multi-lingual parsing faces the challenge of fair comparative evaluation and performance analysis across languages and their treebanks. The difficulty lies in teasing apart the properties of treebanks, such as their size or average sentence length, from those of the annotation scheme, and from the linguistic properties of languages. We propose a method to evaluate the effects of word order of a language on dependency parsing performance, while controlling for confounding treebank properties. The method uses artificially-generated treebanks that are minimal permutations of actual treebanks with respect to two word order properties: word order variation and dependency lengths. Based on these artificial data on twelve languages, we show that longer dependencies and higher word order variability degrade parsing performance. Our method also extends to minimal pairs of individual sentences, leading to a finer-grained understanding of parsing errors.
A Latent Variable Model Approach to PMI-based Word Embeddings
Authors: Arora Sanjeev , Li Yuanzhi , Liang Yingyu , Ma Tengyu , Risteski Andrej
Published: December 2018

Semantic word embeddings represent the meaning of a word via a vector, and are created by diverse methods. Many use nonlinear operations on co-occurrence statistics, and have hand-tuned hyperparameters and reweighting methods.

This paper proposes a new generative model, a dynamic version of the log-linear topic model of Mnih and Hinton (2007). The methodological novelty is to use the prior to compute closed form expressions for word statistics. This provides a theoretical justification for nonlinear models like PMI, word2vec, and GloVe, as well as some hyperparameter choices. It also helps explain why low-dimensional semantic embeddings contain linear algebraic structure that allows solution of word analogies, as shown by Mikolov et al. (2013a) and many subsequent papers.

Experimental support is provided for the generative model assumptions, the most important of which is that latent word vectors are fairly uniformly dispersed in space.
Many Languages, One Parser
Authors: Ammar Waleed , Mulcaire George , Ballesteros Miguel , Dyer Chris , Smith Noah A.
Published: December 2018

We train one multilingual model for dependency parsing and use it to parse sentences in several languages. The parsing model uses (i) multilingual word clusters and embeddings; (ii) token-level language information; and (iii) language-specific features (fine-grained POS tags). This input representation enables the parser not only to parse effectively in multiple languages, but also to generalize across languages based on linguistic universals and typological similarities, making it more effective to learn from limited annotations. Our parser’s performance compares favorably to strong baselines in a range of data scenarios, including when the target language has a large treebank, a small treebank, or no treebank for training.
Fast, Small and Exact: Infinite-order Language Modelling with Compressed Suffix Trees
Authors: Shareghi Ehsan , Petri Matthias , Haffari Gholamreza , Cohn Trevor
Published: December 2018

Efficient methods for storing and querying are critical for scaling high-order m-gram language models to large corpora. We propose a language model based on compressed suffix trees, a representation that is highly compact and can be easily held in memory, while supporting queries needed in computing language model probabilities on-the-fly. We present several optimisations which improve query runtimes up to 2500×, despite only incurring a modest increase in construction time and memory usage. For large corpora and high Markov orders, our method is highly competitive with the state-of-the-art KenLM package. It imposes much lower memory requirements, often by orders of magnitude, and has runtimes that are either similar (for training) or comparable (for querying).
Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependencies
Authors: Linzen Tal , Dupoux Emmanuel , Goldberg Yoav
Published: December 2018

The success of long short-term memory (LSTM) neural networks in language processing is typically attributed to their ability to capture long-distance statistical regularities. Linguistic regularities are often sensitive to syntactic structure; can such dependencies be captured by LSTMs, which do not have explicit structural representations? We begin addressing this question using number agreement in English subject-verb dependencies. We probe the architecture’s grammatical competence both using training objectives with an explicit grammatical target (number prediction, grammaticality judgments) and using language models. In the strongly supervised settings, the LSTM achieved very high overall accuracy (less than 1% errors), but errors increased when sequential and structural information conflicted. The frequency of such errors rose sharply in the language-modeling setting. We conclude that LSTMs can capture a non-trivial amount of grammatical structure given targeted supervision, but stronger architectures may be required to further reduce errors; furthermore, the language modeling signal is insufficient for capturing syntax-sensitive dependencies, and should be supplemented with more direct supervision if such dependencies need to be captured.
Understanding Satirical Articles Using Common-Sense
Authors: Goldwasser Dan , Zhang Xiao
Published: December 2018

Automatic satire detection is a subtle text classification task, for machines and at times, even for humans. In this paper we argue that satire detection should be approached using common-sense inferences, rather than traditional text classification methods. We present a highly structured latent variable model capturing the required inferences. The model abstracts over the specific entities appearing in the articles, grouping them into generalized categories, thus allowing the model to adapt to previously unseen situations.
Efficient Inference and Structured Learning for Semantic Role Labeling
Authors: Täckström Oscar , Ganchev Kuzman , Das Dipanjan
Published: December 2018

We present a dynamic programming algorithm for efficient constrained inference in semantic role labeling. The algorithm tractably captures a majority of the structural constraints examined by prior work in this area, which has resorted to either approximate methods or off-the-shelf integer linear programming solvers. In addition, it allows training a globally-normalized log-linear model with respect to constrained conditional likelihood. We show that the dynamic program is several times faster than an off-the-shelf integer linear programming solver, while reaching the same solution. Furthermore, we show that our structured model results in significant improvements over its local counterpart, achieving state-of-the-art results on both PropBank- and FrameNet-annotated corpora.
Modelling and Optimizing on Syntactic N-Grams for Statistical Machine Translation
Authors: Sennrich Rico
Published: December 2018

The role of language models in SMT is to promote fluent translation output, but traditional n-gram language models are unable to capture fluency phenomena between distant words, such as some morphological agreement phenomena, subcategorisation, and syntactic collocations with string-level gaps. Syntactic language models have the potential to fill this modelling gap. We propose a language model for dependency structures that is relational rather than configurational and thus particularly suited for languages with a (relatively) free word order. It is trainable with Neural Networks, and not only improves over standard n-gram language models, but also outperforms related syntactic language models. We empirically demonstrate its effectiveness in terms of perplexity and as a feature function in string-to-tree SMT from English to German and Russian. We also show that using a syntactic evaluation metric to tune the log-linear parameters of an SMT system further increases translation quality when coupled with a syntactic language model.
Improving Distributional Similarity with Lessons Learned from Word Embeddings
Authors: Levy Omer , Goldberg Yoav , Dagan Ido
Published: December 2018

Recent trends suggest that neural-network-inspired word embedding models outperform traditional count-based distributional models on word similarity and analogy detection tasks. We reveal that much of the performance gains of word embeddings are due to certain system design choices and hyperparameter optimizations, rather than the embedding algorithms themselves. Furthermore, we show that these modifications can be transferred to traditional distributional models, yielding similar gains. In contrast to prior reports, we observe mostly local or insignificant performance differences between the methods, with no global advantage to any single approach over the others.
Learning a Compositional Semantics for Freebase with an Open Predicate Vocabulary
Authors: Krishnamurthy Jayant , Mitchell Tom M.
Published: December 2018

We present an approach to learning a model-theoretic semantics for natural language tied to Freebase. Crucially, our approach uses an open predicate vocabulary, enabling it to produce denotations for phrases such as “Republican front-runner from Texas” whose semantics cannot be represented using the Freebase schema. Our approach directly converts a sentence’s syntactic CCG parse into a logical form containing predicates derived from the words in the sentence, assigning each word a consistent semantics across sentences. This logical form is evaluated against a learned probabilistic database that defines a distribution over denotations for each textual predicate. A training phase produces this probabilistic database using a corpus of entity-linked text and probabilistic matrix factorization with a novel ranking objective function. We evaluate our approach on a compositional question answering task where it outperforms several competitive baselines. We also compare our approach against manually annotated Freebase queries, finding that our open predicate vocabulary enables us to answer many questions that Freebase cannot.
Design Challenges for Entity Linking
Authors: Ling Xiao , Singh Sameer , Weld Daniel S.
Published: December 2018

Recent research on entity linking (EL) has introduced a plethora of promising techniques, ranging from deep neural networks to joint inference. But despite numerous papers there is surprisingly little understanding of the state of the art in EL. We attack this confusion by analyzing differences between several versions of the EL problem and presenting a simple yet effective, modular, unsupervised system, called Vinculum, for entity linking. We conduct an extensive evaluation on nine data sets, comparing Vinculum with two state-of-the-art systems, and elucidate key aspects of the system that include mention extraction, candidate generation, entity type prediction, entity coreference, and coherence.
Deriving Boolean structures from distributional vectors
Authors: Kruszewski German , Paperno Denis , Baroni Marco
Published: December 2018

Corpus-based distributional semantic models capture degrees of semantic relatedness among the words of very large vocabularies, but have problems with logical phenomena such as entailment, that are instead elegantly handled by model-theoretic approaches, which, in turn, do not scale up. We combine the advantages of the two views by inducing a mapping from distributional vectors of words (or sentences) into a Boolean structure of the kind in which natural language terms are assumed to denote. We evaluate this Boolean Distributional Semantic Model (BDSM) on recognizing entailment between words and sentences. The method achieves results comparable to a state-of-the-art SVM, degrades more gracefully when less training data are available and displays interesting qualitative properties.
A Hierarchical Distance-dependent Bayesian Model for Event Coreference Resolution
Authors: Yang Bishan , Cardie Claire , Frazier Peter
Published: December 2018

We present a novel hierarchical distance-dependent Bayesian model for event coreference resolution. While existing generative models for event coreference resolution are completely unsupervised, our model allows for the incorporation of pairwise distances between event mentions — information that is widely used in supervised coreference models to guide the generative clustering processing for better event clustering both within and across documents. We model the distances between event mentions using a feature-rich learnable distance function and encode them as Bayesian priors for nonparametric clustering. Experiments on the ECB+ corpus show that our model outperforms state-of-the-art methods for both within- and cross-document event coreference resolution.
Semantic Parsing of Ambiguous Input through Paraphrasing and Verification
Authors: Arthur Philip , Neubig Graham , Sakti Sakriani , Toda Tomoki , Nakamura Satoshi
Published: December 2018

We propose a new method for semantic parsing of ambiguous and ungrammatical input, such as search queries. We do so by building on an existing semantic parsing framework that uses synchronous context free grammars (SCFG) to jointly model the input sentence and output meaning representation. We generalize this SCFG framework to allow not one, but multiple outputs. Using this formalism, we construct a grammar that takes an ambiguous input string and jointly maps it into both a meaning representation and a natural language paraphrase that is less ambiguous than the original input. This paraphrase can be used to disambiguate the meaning representation via verification using a language model that calculates the probability of each paraphrase.
Segmentation for Efficient Supervised Language Annotation with an Explicit Cost-Utility Tradeoff
Authors: Sperber Matthias , Simantzik Mirjam , Neubig Graham , Nakamura Satoshi , Waibel Alex
Published: December 2018

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.
Entity Linking on Microblogs with Spatial and Temporal Signals
Authors: Fang Yuan , Chang Ming-Wei
Published: December 2018

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.
Multi-Modal Models for Concrete and Abstract Concept Meaning
Authors: Hill Felix , Reichart Roi , Korhonen Anna
Published: December 2018

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: Passonneau Rebecca J. , Carpenter Bob
Published: December 2018

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.
Locally Non-Linear Learning for Statistical Machine Translation via Discretization and Structured Regularization
Authors: Clark Jonathan H. , Dyer Chris , Lavie Alon
Published: December 2018

Linear models, which support efficient learning and inference, are the workhorses of statistical machine translation; however, linear decision rules are less attractive from a modeling perspective. In this work, we introduce a technique for learning arbitrary, rule-local, non-linear feature transforms that improve model expressivity, but do not sacrifice the efficient inference and learning associated with linear models. To demonstrate the value of our technique, we discard the customary log transform of lexical probabilities and drop the phrasal translation probability in favor of raw counts. We observe that our algorithm learns a variation of a log transform that leads to better translation quality compared to the explicit log transform. We conclude that non-linear responses play an important role in SMT, an observation that we hope will inform the efforts of feature engineers.
Learning Strictly Local Subsequential Functions
Authors: Chandlee Jane , Eyraud Rémi , Heinz Jeffrey
Published: December 2018

We define two proper subclasses of subsequential functions based on the concept of Strict Locality (McNaughton and Papert, 1971; Rogers and Pullum, 2011; Rogers et al., 2013) for formal languages. They are called Input and Output Strictly Local (ISL and OSL). We provide an automata-theoretic characterization of the ISL class and theorems establishing how the classes are related to each other and to Strictly Local languages. We give evidence that local phonological and morphological processes belong to these classes. Finally we provide a learning algorithm which provably identifies the class of ISL functions in the limit from positive data in polynomial time and data. We demonstrate this learning result on appropriately synthesized artificial corpora. We leave a similar learning result for OSL functions for future work and suggest future directions for addressing non-local phonological processes.
Exploring Compositional Architectures and Word Vector Representations for Prepositional Phrase Attachment
Authors: Belinkov Yonatan , Lei Tao , Barzilay Regina , Globerson Amir
Published: December 2018

Prepositional phrase (PP) attachment disambiguation is a known challenge in syntactic parsing. The lexical sparsity associated with PP attachments motivates research in word representations that can capture pertinent syntactic and semantic features of the word. One promising solution is to use word vectors induced from large amounts of raw text. However, state-of-the-art systems that employ such representations yield modest gains in PP attachment accuracy.

In this paper, we show that word vector representations can yield significant PP attachment performance gains. This is achieved via a non-linear architecture that is discriminatively trained to maximize PP attachment accuracy. The architecture is initialized with word vectors trained from unlabeled data, and relearns those to maximize attachment accuracy. We obtain additional performance gains with alternative representations such as dependency-based word vectors. When tested on both English and Arabic datasets, our method outperforms both a strong SVM classifier and state-of-the-art parsers. For instance, we achieve 82.6% PP attachment accuracy on Arabic, while the Turbo and Charniak self-trained parsers obtain 76.7% and 80.8% respectively.
A Novel Feature-based Bayesian Model for Query Focused Multi-document Summarization
Authors: Li Jiwei , Li Sujian
Published: December 2018

Supervised learning methods and LDA based topic model have been successfully applied in the field of multi-document summarization. In this paper, we propose a novel supervised approach that can incorporate rich sentence features into Bayesian topic models in a principled way, thus taking advantages of both topic model and feature based supervised learning methods. Experimental results on DUC2007, TAC2008 and TAC2009 demonstrate the effectiveness of our approach.
Incremental Tree Substitution Grammar for Parsing and Sentence Prediction
Authors: Sangati Federico , Keller Frank
Published: December 2018

In this paper, we present the first incremental parser for Tree Substitution Grammar (TSG). A TSG allows arbitrarily large syntactic fragments to be combined into complete trees; we show how constraints (including lexicalization) can be imposed on the shape of the TSG fragments to enable incremental processing. We propose an efficient Earley-based algorithm for incremental TSG parsing and report an F-score competitive with other incremental parsers. In addition to whole-sentence F-score, we also evaluate the partial trees that the parser constructs for sentence prefixes; partial trees play an important role in incremental interpretation, language modeling, and psycholinguistics. Unlike existing parsers, our incremental TSG parser can generate partial trees that include predictions about the upcoming words in a sentence. We show that it outperforms an n-gram model in predicting more than one upcoming word.
Modeling Semantic Relations Expressed by Prepositions
Authors: Srikumar Vivek , Roth Dan
Published: December 2018

This paper introduces the problem of predicting semantic relations expressed by prepositions and develops statistical learning models for predicting the relations, their arguments and the semantic types of the arguments. We define an inventory of 32 relations, building on the word sense disambiguation task for prepositions and collapsing related senses across prepositions. Given a preposition in a sentence, our computational task to jointly model the preposition relation and its arguments along with their semantic types, as a way to support the relation prediction. The annotated data, however, only provides labels for the relation label, and not the arguments and types. We address this by presenting two models for preposition relation labeling. Our generalization of latent structure SVM gives close to 90% accuracy on relation labeling. Further, by jointly predicting the relation, arguments, and their types along with preposition sense, we show that we can not only improve the relation accuracy, but also significantly improve sense prediction accuracy.
Unsupervised Tree Induction for Tree-based Translation
Authors: Zhai Feifei , Zhang Jiajun , Zhou Yu , Zong Chengqing
Published: December 2018

In current research, most tree-based translation models are built directly from parse trees. In this study, we go in another direction and build a translation model with an unsupervised tree structure derived from a novel non-parametric Bayesian model. In the model, we utilize synchronous tree substitution grammars (STSG) to capture the bilingual mapping between language pairs. To train the model efficiently, we develop a Gibbs sampler with three novel Gibbs operators. The sampler is capable of exploring the infinite space of tree structures by performing local changes on the tree nodes. Experimental results show that the string-to-tree translation system using our Bayesian tree structures significantly outperforms the strong baseline string-to-tree system using parse trees.
Good, Great, Excellent: Global Inference of Semantic Intensities
Authors: de Melo Gerard , Bansal Mohit
Published: December 2018

Adjectives like good, great, and excellent are similar in meaning, but differ in intensity. Intensity order information is very useful for language learners as well as in several NLP tasks, but is missing in most lexical resources (dictionaries, WordNet, and thesauri). In this paper, we present a primarily unsupervised approach that uses semantics from Web-scale data (e.g., phrases like good but not excellent) to rank words by assigning them positions on a continuous scale. We rely on Mixed Integer Linear Programming to jointly determine the ranks, such that individual decisions benefit from global information. When ranking English adjectives, our global algorithm achieves substantial improvements over previous work on both pairwise and rank correlation metrics (specifically, 70% pairwise accuracy as compared to only 56% by previous work). Moreover, our approach can incorporate external synonymy information (increasing its pairwise accuracy to 78%) and extends easily to new languages. We also make our code and data freely available.
Data-driven, PCFG-based and Pseudo-PCFG-based Models for Chinese Dependency Parsing
Authors: Sun Weiwei , Wan Xiaojun
Published: December 2018

We present a comparative study of transition-, graph- and PCFG-based models aimed at illuminating more precisely the likely contribution of CFGs in improving Chinese dependency parsing accuracy, especially by combining heterogeneous models. Inspired by the impact of a constituency grammar on dependency parsing, we propose several strategies to acquire pseudo CFGs only from dependency annotations. Compared to linguistic grammars learned from rich phrase-structure treebanks, well designed pseudo grammars achieve similar parsing accuracy and have equivalent contributions to parser ensemble. Moreover, pseudo grammars increase the diversity of base models; therefore, together with all other models, further improve system combination. Based on automatic POS tagging, our final model achieves a UAS of 87.23%, resulting in a significant improvement of the state of the art.
Powergrading: a Clustering Approach to Amplify Human Effort for Short Answer Grading
Authors: Basu Sumit , Jacobs Chuck , Vanderwende Lucy
Published: December 2018

We introduce a new approach to the machine-assisted grading of short answer questions. We follow past work in automated grading by first training a similarity metric between student responses, but then go on to use this metric to group responses into clusters and subclusters. The resulting groupings allow teachers to grade multiple responses with a single action, provide rich feedback to groups of similar answers, and discover modalities of misunderstanding among students; we refer to this amplification of grader effort as “powergrading.” We develop the means to further reduce teacher effort by automatically performing actions when an answer key is available. We show results in terms of grading progress with a small “budget” of human actions, both from our method and an LDA-based approach, on a test corpus of 10 questions answered by 698 respondents.
Training Deterministic Parsers with Non-Deterministic Oracles
Authors: Goldberg Yoav , Nivre Joakim
Published: December 2018

Greedy transition-based parsers are very fast but tend to suffer from error propagation. This problem is aggravated by the fact that they are normally trained using oracles that are deterministic and incomplete in the sense that they assume a unique canonical path through the transition system and are only valid as long as the parser does not stray from this path. In this paper, we give a general characterization of oracles that are nondeterministic and complete, present a method for deriving such oracles for transition systems that satisfy a property we call arc decomposition, and instantiate this method for three well-known transition systems from the literature. We say that these oracles are dynamic, because they allow us to dynamically explore alternative and nonoptimal paths during training — in contrast to oracles that statically assume a unique optimal path. Experimental evaluation on a wide range of data sets clearly shows that using dynamic oracles to train greedy parsers gives substantial improvements in accuracy. Moreover, this improvement comes at no cost in terms of efficiency, unlike other techniques like beam search.
Measuring Machine Translation Errors in New Domains
Authors: Irvine Ann , Morgan John , Carpuat Marine , Daumé Hal , Munteanu Dragos
Published: December 2018

We develop two techniques for analyzing the effect of porting a machine translation system to a new domain. One is a macro-level analysis that measures how domain shift affects corpus-level evaluation; the second is a micro-level analysis for word-level errors. We apply these methods to understand what happens when a Parliament-trained phrase-based machine translation system is applied in four very different domains: news, medical texts, scientific articles and movie subtitles. We present quantitative and qualitative experiments that highlight opportunities for future research in domain adaptation for machine translation.
Erratum: “ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs”
Authors: Yin Wenpeng , Schütze Hinrich , Xiang Bing , Zhou Bowen
Published: December 2018

In this erratum, we correct the lack of proper attribution of two quotations.
“Palaeoentomology”: A modern journal for a science dealing with the past

Authors: AZAR DANY , SZWEDO JACEK , JARZEMBOWSKI EDMUND , EVENHUIS NEAL , HUANG DIYING
Published: December 2018

Palaeoentomology started in the late XVIIIth century, shortly after the 10th edition of Linnaeus’ Systema Naturae (the foundation of modern taxonomy), when papers on the curiosities of insects entombed in fossil resins were published. The beginning of XIXth century (with the growing interest in geological sciences and prehistoric life) witnessed the first attempts to study and describe insects from sedimentary rocks. This discipline then developed during the XIXth and beginning of the XXth centuries; and resulted in some major works and reviews (summarizing the knowledge on fossil insects and other terrestrial arthropods) published in the geological and biological literature. The XXth century was a period of relatively slow but constant development in palaeoentomology, during which the famous “Treatise on invertebrate paleontology: Arthropoda 4. Superclass Hexapoda” (cataloguing the knowledge on fossil insects) was published (Carpenter, 1992). At the beginning of XXIst century, palaeoentomology grew significantly and exponentially; and two major manuals (“History of insects” and “Evolution of the insects”) were published (Rasnitsyn & Quicke, 2002; Grimaldi & Engel, 2005, respectively). These manuals helped to encourage more students and researchers to work on fossil insects and other terrestrial arthropods.
Various amberground marine animals on Burmese amber with discussions on its age

Authors: MAO YINGYAN , LIANG KUN , SU YITONG , LI JIANGUO , RAO XIN , ZHANG HUA , XIA FANGYUAN , FU YANZHE , CAI CHENYANG , HUANG DIYING
Published: December 2018

Burmese amber represents the world’s most diverse biota in the Mesozoic. Previous studies have focused on the biodiversity of its inclusions, as well as pholadid borings. Here we report a variety of marine animals symbiotic with or adhere to Burmese amber or the amber deposits, including crinoid columns, corals and oysters. We propose that there is no distinct evidence indicating the secondary transportation of Burmese amber over long distances. The ancient sedimentary environment was likely located in the coastal area. The hardening time of the resin was not long after secretion. The resin has been mixed with fragments of marine organisms in the ancient sediments, and has been deposited for a long time. The zircon age in the sediments surrounding amber approximately represents the age of Burmese amber, but due to limits of the method, the current zircon U-Pb SIMS age may be younger. Therefore, as far as the situation is concerned, the age of Burmese amber may be close to the boundary between the Albian and Cenomanian, or even late Albian. We suggest that it is plausible to generally refer to the age of Burmese amber as mid-Cretaceous, and a precise age requires further biostratigraphic and chronological studies.
Sporolithon mesophoticum sp. nov. (Sporolithales, Rhodophyta) from Plantagenet Bank off Bermuda at a depth of 178 m
Authors: RICHARDS JOSEPH L. , GABRIELSON PAUL W. , SCHNEIDER CRAIG W.
Published: December 2018

A coralline rhodolith was collected from a depth of 178 m, the lower end of the mesophotic zone, on Plantagenet Bank offshore of Bermuda by the submersible Nomad using a mechanical armature. DNA sequence comparisons of the plastid encoded psbA and rbcL loci of this specimen to Sporolithales from the tropical western Atlantic and worldwide revealed it to be an unnamed species of Sporolithon. Sporolithon mesophoticum sp. nov. possesses tetrasporangial sori flush to slightly raised above the thallus surface and has uniquely flattened perithallial and meristematic cells. Thus far, the new species is the deepest known living marine macroalga that has been sequenced and placed into a phylogenetic context.
Accelerating studies on the ontogeny and morphological diversity in immature mites
Authors: ZHANG ZHI-QIANG
Published: December 2018

Over 58,000 species of mites have been described (Zhang 2011; Liu et al. 2013). Most species of mites are known only as adults, although immature instars also present a diversity of characters and other information potentially useful for understanding mite taxonomy, phylogeny and biology. A recent overview of juvenile instars of oribatid mites (excluding Astigmata) shows that only about 8% of the known oribatid mites have one or more juvenile instars described (Norton & Ermilov 2014). A survey of papers published during the last three years (2015–2017) in five main acarological journals (Acarina, Acarologia, International Journal of Acarology, Persian Journal of Acarology, Systematic and Applied Acarology) and two other main zoological journals (Zootaxa and ZooKeys) with mite papers showed only 3% to 19% of these with descriptions of immatures, but among the 151 species included in these papers as many as 61% have full data on the ontogeny (Liu & Zhang 2018).
A new Argentinian species of Bembidion (Chilioperyphus) (Coleoptera: Carabidae: Bembidiini) with accessory setae
Authors: ROIG-JUÑENT SERGIO , SCHEIBLER ERICA
Published: December 2018

The new species, Bembidion (Chilioperyphus) cuyanus (type locality ARGENTINA, San Luis, Quebrada Lopez, 7 km SE San Francisco) is described, based on external structural features (extra setae in the supraorbital region, pronotum, elytral margin and intervals 3, 5 and 7) and those of the male genitalia (details of the endophallic flagellum). Line drawings illustrate diagnostic features and a key distinguishes among the species of subgenus Chilioperyphus Jeannel. This species lives along the border of rivers and creeks in the arid region of western Argentina.KEY WORDS. Carabidae. Bembidiini. Bembidion (Chilioperyphus) cuyanus n. sp., ArgentinaSe describe una nueva especie del g nero Bembidion Latreille (Coleoptera: Carabidae: Bembidiini) del centro oeste de la Argentina. Esta ha sido encontrada en las orillas de arroyos y r os de esta regi n semides rtica. La morfolog a externa y las caracter sticas de las estructuras de la genitalia masculina hacen considerar que esta nueva especie pertenece al g nero Bembidion Latreille, y algunos caracteres del ed ago muestran que pertenece al subg nero Chilioperyphus Jeannel. La estructura del flagelo y la presencia de numerosas setas, tanto en la regi n supraorbital, pronoto, borde del litro e interesar as 3, 5 y 7, permiten reconocer a esta como una nueva especie, Bembidion (Chilioperyphus) cuyanus nov. sp. Se provee la descripci n e ilustraciones de esta nueva especie, como as tambi n una clave para diferenciarla de las restantes especies del subg nero Chilioperyphus.PALABRAS CLAVE. Carabidae. Bembidiini. Bembidion (Chilioperyphus) cuyanus n. sp., Argentina
A new hypogeal species of Oregmopyga Hoy (Hemiptera: Coccoidea: Eriococcidae) from southern California, U.S.A., and a key to species of the genus
Authors: KONDO TAKUMASA , GULLAN PENNY J. , MILLER DOUGLASS R.
Published: December 2018

A new species of hypogeal eriococcid, Oregmopyga viscosa Kondo sp. nov., is described and illustrated based on the adult female and the second-instar male and female. This species is known from Inyo, Kern, Riverside, San Bernardino, and San Diego counties in southern California, where it lives just below the soil surface on the crown of its host, probably always Hymenoclea salsola Torr. & A. Gray (Asteraceae). A revised key to the adult females of all nine species of Oregmopyga is included.Una nueva especie de erioc ccido subterraneo, Oregmopyga viscosa Kondo sp. nov. (Hemiptera: Coccoidea), se describe e ilustra basandose en la hembra adulta y el segundo estadio de la hembra y el macho. Esta especie ha sido encontrada en los condados Inyo, Kern, Riverside, San Bernardino y San Diego en el sur de California. La especie vive cerca de la superficie en el tallo de su hospedero, siempre siendo probablemente Hymenoclea salsola Torr. & A. Gray (Asteraceae). Se incluye una clave revisada para las hembras adultas de todas las nueve especies de Oregmopyga.
Ceraspis jaliscoensis, a new species from México (Coleoptera: Scarabaeidae: Melolonthinae)
Authors: DELGADO LEONARDO , NAVARRETE-HEREDIA JOSÉ LUIS
Published: December 2018

A new Mexican species of Ceraspis LePeletier and Serville, C. jaliscoensis, from Jalisco state is described, and its male genitalia are illustrated. A key to the Mexican species of this genus and distribution maps of those species are included.
A new species of Schinia H bner from riparian habitats in the Grand Canyon (Lepidoptera: Noctuidae: Heliothinae)
Authors: POGUE MICHAEL G.
Published: December 2018

Schinia immaculata, new species, is described from riparian habitats along the Colorado River in the Grand Canyon. Habitats include the shoreline, new high water dominated by tamarisk (Tamarix sp., Tamaricaceae), and old high water characterized by mesquite (Prosopis sp., Fabaceae), acacia (Acacia sp., Fabaceae), and desert shrubs. Adult and male genitalia are illustrated and compared with Schinia biundulata Smith.
A redescription of the carinate pillsnail, Euchemotrema hubrichti (Pilsbry, 1940) (Pulmonata: Polygyridae), with notes on habitat and genetics
Authors: ANDERSON FRANK E. , SMITH DAVID A.
Published: December 2018

Euchemotrema hubrichti (Pilsbry, 1940) was initially described as Stenotrema hubrichti by Pilsbry (1940). The species was known only from shell material, and it was originally thought to be extinct. Live specimens have since been found, but the soft-part anatomy, radula and jaws of E. hubrichti have never been described. This species is of substantial conservation concern, primarily due to our lack of knowledge about the species and its highly restricted distribution. We redescribe E. hubrichti based on material freshly collected from the type locality. Euchemotrema hubrichti is found at several sites in the Larue–Pine Hills Research Natural Area in Union County, Illinois, U.S.A., and is often abundant under loose slabs on the tops of relatively dry limestone outcrops. The shell of E. hubrichti differs from those of E. fraternum (Say, 1824) by its acute keel and low number of whorls. The genitalia of E. hubrichti are similar to those of S. stenotrema (Pfeiffer, 1842), but show a diagnostic penile inflation and lumenal twisting of the free oviduct. Partial mitochondrial cytochrome c oxidase subunit I nucleotide sequences generated from four E. hubrichti individuals are identical, and show high similarity to available stylommatophoran sequences.
Description of a new species belonging to the genus Leofa Distant (Hemiptera: Cicadellidae: Deltocephalinae: Stenometopiini) from India
Authors: CHALAM M.S.V. , RAO CHALAM
Published: December 2018

A new species, Leofa mushroomi, belonging to the genus Leofa Distant (Hemiptera: Cicadellidae: Deltocephalinae: Stenometopiini) from India is described and illustrated. A key to the Indian species of Leofa is included.
Study on the dynamic vibration characteristics of viscoelastic yarn bundles
Authors: Xu Yang , Cheng Furong , Sheng Xiaowei , Angang Li , Thiaw Ahmadou Bamba Sourang
Published: December 2018

In order to study the influence of yarn bundle vibration characteristics on the vibration and noise of tufted carpet looms, a yarn bundle vibration model was proposed in this paper, which was based on the viscoelasticity of the yarn bundle, and the correctness of the transverse vibration equation of the yarn bundle was verified by experiments. Different creep models of the yarn bundle were fitted with the experimental data, and the transverse vibration equation of the axial motion viscoelastic yarn bundle was established by using Burgers four-element constitutive model. Then, the Galerkin truncation method was used to solve the partial differential vibration equation of the yarn bundle and solve the equation. Finally, the correctness of the vibration equation is verified by comparison between the experimental results and the numerical simulation results. The results show that the vibration equation is suitable for studying the transverse dynamic vibration characteristics of the yarn bundle.
When Did Wendell Berry Start Talking Like a Christian?
Authors: Bilbro Jeffrey
Published: December 2018

Around 1979, Wendell Berry began using more explicitly Christian language to articulate his view of creation. This essay traces Berry’s youthful rebellion from and eventual return to Christian language through his letters with Snyder and his essay “The Gift of Good Land.” It then contrasts the sacramental vision his early sabbath poems articulate with the pagan vision of several early poems—poems he chooses not to include in his 1985 Collected Poems. Finally, it considers how Remembering, his first novel focusing on the autobiographical character Andy Catlett, can be read as a conversion narrative.
Triangulated perspectives on outcomes of pulmonary rehabilitation in patients with COPD: a qualitative study to inform a core outcome set
Authors: Souto-Miranda Sara , Marques Alda
Published: December 2018

Introduction:

Pulmonary rehabilitation implies a comprehensive assessment. Although several outcomes are commonly measured, those are selected mainly by health professionals and researchers, with the voice of patients and informal caregivers being minimally captured. Qualitative studies are fundamental to enhance our knowledge on perspectives of different stakeholders involved in pulmonary rehabilitation.

Objective:

This study aimed to explore the views of different stakeholders on outcomes of pulmonary rehabilitation, contributing to one of the stages of a core outcome set for pulmonary rehabilitation in patients with chronic obstructive pulmonary disease (COPD).

Methods:

Semi-structured interviews were conducted with 12 patients with COPD, 11 informal carers and 10 health professionals. Data were analysed with content analysis, followed by thematic analysis to gain deeper understanding of the different perspectives.

Results:

A total of 44 outcomes were identified, being the most reported ‘improving functional performance’ (67%) and ‘reducing and taking control over dyspnoea’ (64%). Five relevant themes across stakeholders were generated: having a healthy mind in a healthy body; I can(’t) do it; feeling fulfilled; knowing more, doing better and avoiding doctors and expenses. Although perspectives were mostly consensual, some outcomes were only valued by health professionals (e.g. pulmonary function) or by patients and informal carers (e.g. quality of sleep).

Conclusion:

Views of the different stakeholders on outcomes of pulmonary rehabilitation were similar although, some specificities existed. Comprehensive assessments are needed to reflect what is valued by the different stakeholders in pulmonary rehabilitation. This study contributed to a future core outcome set in this field.