Using supertags as source language context in SMT

Recent research has shown that Phrase-Based Statistical Machine Translation (PB-SMT) systems can benefit from two enhancements: (i) using words and POS tags as context-informed features on the source side; and (ii) incorporating lexical syntactic descriptions in the form of supertags on the target side. In this work we present a novel PB-SMT model that combines these two aspects by using supertags as source language contextinformed features. These features enable us to exploit source similarity in addition to target similarity, as modelled by the language model. In our experiments two kinds of supertags are employed: those from Lexicalized Tree-Adjoining Grammar and Combinatory Categorial Grammar. We use a memory-based classification framework that enables the estimation of these features while avoiding problems of sparseness. Despite the differences between these two approaches, the supertaggers give similar improvements. We evaluate the performance of our approach on an English-to-Chinese translation task using a state-of-the-art phrase-based SMT system, and report an improvement of 7.88% BLEU score in translation quality when adding supertags as context-informed features

Complexity of Lexical Descriptions and its Relevance to Partial Parsing

In this dissertation, we have proposed novel methods for robust parsing that integrate the flexibility of linguistically motivated lexical descriptions with the robustness of statistical techniques. Our thesis is that the computation of linguistic structure can be localized if lexical items are associated with rich descriptions (supertags) that impose complex constraints in a local context. However, increasing the complexity of descriptions makes the number of different descriptions for each lexical item much larger and hence increases the local ambiguity for a parser. This local ambiguity can be resolved by using supertag co-occurrence statistics collected from parsed corpora. We have explored these ideas in the context of Lexicalized Tree-Adjoining Grammar (LTAG) framework wherein supertag disambiguation provides a representation that is an almost parse. We have used the disambiguated supertag sequence in conjunction with a lightweight dependency analyzer to compute noun groups, verb groups, dependency linkages and even partial parses. We have shown that a trigram-based supertagger achieves an accuracy of 92.1‰ on Wall Street Journal (WSJ) texts. Furthermore, we have shown that the lightweight dependency analysis on the output of the supertagger identifies 83‰ of the dependency links accurately. We have exploited the representation of supertags with Explanation-Based Learning to improve parsing effciency. In this approach, parsing in limited domains can be modeled as a Finite-State Transduction. We have implemented such a system for the ATIS domain which improves parsing eciency by a factor of 15. We have used the supertagger in a variety of applications to provide lexical descriptions at an appropriate granularity. In an information retrieval application, we show that the supertag based system performs at higher levels of precision compared to a system based on part-of-speech tags. In an information extraction task, supertags are used in specifying extraction patterns. For language modeling applications, we view supertags as syntactically motivated class labels in a class-based language model. The distinction between recursive and non-recursive supertags is exploited in a sentence simplification application

Integrating source-language context into log-linear models of statistical machine translation

The translation features typically used in state-of-the-art statistical machine translation (SMT) model dependencies between the source and target phrases, but not among the phrases in the source language themselves. A swathe of research has demonstrated that integrating source context modelling directly into log-linear phrase-based SMT (PB-SMT) and hierarchical PB-SMT (HPB-SMT), and can positively influence the weighting and selection of target phrases, and thus improve translation quality. In this thesis we present novel approaches to incorporate source-language contextual modelling into the state-of-the-art SMT models in order to enhance the quality of lexical selection. We investigate the effectiveness of use of a range of contextual features, including lexical features of neighbouring words, part-of-speech tags, supertags, sentence-similarity features, dependency information, and semantic roles. We explored a series of language pairs featuring typologically different languages, and examined the scalability of our research to larger amounts of training data. While our results are mixed across feature selections, language pairs, and learning curves, we observe that including contextual features of the source sentence in general produces improvements. The most significant improvements involve the integration of long-distance contextual features, such as dependency relations in combination with part-of-speech tags in Dutch-to-English subtitle translation, the combination of dependency parse and semantic role information in English-to-Dutch parliamentary debate translation, supertag features in English-to-Chinese translation, or combination of supertag and lexical features in English-to-Dutch subtitle translation. Furthermore, we investigate the applicability of our lexical contextual model in another closely related NLP problem, namely machine transliteration

Research in the Language, Information and Computation Laboratory of the University of Pennsylvania

This report takes its name from the Computational Linguistics Feedback Forum (CLiFF), an informal discussion group for students and faculty. However the scope of the research covered in this report is broader than the title might suggest; this is the yearly report of the LINC Lab, the Language, Information and Computation Laboratory of the University of Pennsylvania. It may at first be hard to see the threads that bind together the work presented here, work by faculty, graduate students and postdocs in the Computer Science and Linguistics Departments, and the Institute for Research in Cognitive Science. It includes prototypical Natural Language fields such as: Combinatorial Categorial Grammars, Tree Adjoining Grammars, syntactic parsing and the syntax-semantics interface; but it extends to statistical methods, plan inference, instruction understanding, intonation, causal reasoning, free word order languages, geometric reasoning, medical informatics, connectionism, and language acquisition. Naturally, this introduction cannot spell out all the connections between these abstracts; we invite you to explore them on your own. In fact, with this issue it’s easier than ever to do so: this document is accessible on the “information superhighway”. Just call up http://www.cis.upenn.edu/~cliff-group/94/cliffnotes.html In addition, you can find many of the papers referenced in the CLiFF Notes on the net. Most can be obtained by following links from the authors’ abstracts in the web version of this report. The abstracts describe the researchers’ many areas of investigation, explain their shared concerns, and present some interesting work in Cognitive Science. We hope its new online format makes the CLiFF Notes a more useful and interesting guide to Computational Linguistics activity at Penn

Statistical Deep parsing for spanish

This document presents the development of a statistical HPSG parser for Spanish. HPSG is a deep linguistic formalism that combines syntactic and semanticinformation in the same representation, and is capable of elegantly modelingmany linguistic phenomena. Our research consists in the following steps: design of the HPSG grammar, construction of the corpus, implementation of theparsing algorithms, and evaluation of the parsers performance. We created a simple yet powerful HPSG grammar for Spanish that modelsmorphosyntactic information of words, syntactic combinatorial valence, and semantic argument structures in its lexical entries. The grammar uses thirteenvery broad rules for attaching specifiers, complements, modifiers, clitics, relative clauses and punctuation symbols, and for modeling coordinations. In asimplification from standard HPSG, the only type of long range dependency wemodel is the relative clause that modifies a noun phrase, and we use semanticrole labeling as our semantic representation. We transformed the Spanish AnCora corpus using a semi-automatic processand analyzed it using our grammar implementation, creating a Spanish HPSGcorpus of 517,237 words in 17,328 sentences (all of AnCora). We implemented several statistical parsing algorithms and trained them overthis corpus. The implemented strategies are: a bottom-up baseline using bi-lexical comparisons or a multilayer perceptron; a CKY approach that uses theresults of a supertagger; and a top-down approach that encodes word sequencesusing a LSTM network. We evaluated the performance of the implemented parsers and compared them with each other and against other existing Spanish parsers. Our LSTM top-down approach seems to be the best performing parser over our test data, obtaining the highest scores (compared to our strategies and also to externalparsers) according to constituency metrics (87.57 unlabeled F1, 82.06 labeled F1), dependency metrics (91.32 UAS, 88.96 LAS), and SRL (87.68 unlabeled,80.66 labeled), but we must take in consideration that the comparison against the external parsers might be noisy due to the post-processing we needed to do in order to adapt them to our format. We also defined a set of metrics to evaluate the identification of some particular language phenomena, and the LSTM top-down parser out performed the baselines in almost all of these metrics as well.Este documento presenta el desarrollo de un parser HPSG estadístico para el español. HPSG es un formalismo lingüístico profundo que combina información sintáctica y semántica en sus representaciones, y es capaz de modelar elegantemente una buena cantidad de fenómenos lingüísticos. Nuestra investigación se compone de los siguiente pasos: diseño de la gramática HPSG, construcción del corpus, implementación de los algoritmos de parsing y evaluación de la performance de los parsers. Diseñamos una gramática HPSG para el español simple y a la vez poderosa, que modela en sus entradas léxicas la información morfosintáctica de las palabras, la valencia combinatoria sintáctica y la estructura argumental semántica. La gramática utiliza trece reglas genéricas para adjuntar especificadores, complementos, clíticos, cláusulas relativas y símbolos de puntuación, y también para modelar coordinaciones. Como simplificación de la teoría HPSG estándar, el único tipo de dependencia de largo alcance que modelamos son las cláusulas relativas que modifican sintagmas nominales, y utilizamos etiquetado de roles semánticos como representación semántica. Transformamos el corpus AnCora en español utilizando un proceso semiautomático y lo analizamos mediante nuestra implementación de la gramática, para crear un corpus HPSG en español de 517,237 palabras en 17,328 oraciones (todo el contenido de AnCora). Implementamos varios algoritmos de parsing estadístico entrenados sobre este corpus. En particular, teníamos como objetivo probar enfoques basados en redes neuronales. Las estrategias implementadas son: una línea base bottom-up que utiliza comparaciones bi-léxicas o un perceptrón multicapa; un enfoque tipo CKY que utiliza los resultados de un supertagger; y un enfoque top-down que codifica las secuencias de palabras mediante redes tipo LSTM. Evaluamos la performance de los parsers implementados y los comparamos entre sí y con un conjunto de parsers existententes para el español. Nuestro enfoque LSTM top-down parece ser el que tiene mejor desempeño para nuestro conjunto de test, obteniendo los mejores puntajes (comparado con nuestras estrategias y también con parsers externos) en cuanto a métricas de constituyentes (87.57 F1 no etiquetada, 82.06 F1 etiquetada), métricas de dependencias (91.32 UAS, 88.96 LAS), y SRL (87.68 no etiquetada, 80.66 etiquetada), pero debemos tener en cuenta que la comparación con parsers externos puede ser ruidosa debido al post procesamiento realizado para adaptarlos a nuestro formato. También definimos un conjunto de métricas para evaluar la identificación de algunos fenómenos particulares del lenguaje, y el parser LSTM top-down obtuvo mejores resultados que las baselines para casi todas estas métricas

Porting a lexicalized-grammar parser to the biomedical domain

AbstractThis paper introduces a state-of-the-art, linguistically motivated statistical parser to the biomedical text mining community, and proposes a method of adapting it to the biomedical domain requiring only limited resources for data annotation. The parser was originally developed using the Penn Treebank and is therefore tuned to newspaper text. Our approach takes advantage of a lexicalized grammar formalism, Combinatory Categorial Grammar (ccg), to train the parser at a lower level of representation than full syntactic derivations. The ccg parser uses three levels of representation: a first level consisting of part-of-speech (pos) tags; a second level consisting of more fine-grained ccg lexical categories; and a third, hierarchical level consisting of ccg derivations. We find that simply retraining the pos tagger on biomedical data leads to a large improvement in parsing performance, and that using annotated data at the intermediate lexical category level of representation improves parsing accuracy further. We describe the procedure involved in evaluating the parser, and obtain accuracies for biomedical data in the same range as those reported for newspaper text, and higher than those previously reported for the biomedical resource on which we evaluate. Our conclusion is that porting newspaper parsers to the biomedical domain, at least for parsers which use lexicalized grammars, may not be as difficult as first thought

Paracompositionality, MWEs and Argument Substitution

Multi-word expressions, verb-particle constructions, idiomatically combining phrases, and phrasal idioms have something in common: not all of their elements contribute to the argument structure of the predicate implicated by the expression. Radically lexicalized theories of grammar that avoid string-, term-, logical form-, and tree-writing, and categorial grammars that avoid wrap operation, make predictions about the categories involved in verb-particles and phrasal idioms. They may require singleton types, which can only substitute for one value, not just for one kind of value. These types are asymmetric: they can be arguments only. They also narrowly constrain the kind of semantic value that can correspond to such syntactic categories. Idiomatically combining phrases do not subcategorize for singleton types, and they exploit another locally computable and compositional property of a correspondence, that every syntactic expression can project its head word. Such MWEs can be seen as empirically realized categorial possibilities, rather than lacuna in a theory of lexicalizable syntactic categories.Comment: accepted version (pre-final) for 23rd Formal Grammar Conference, August 2018, Sofi