58 research outputs found
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 itâs easier than ever to do so: this document is accessible on the âinformation superhighwayâ. 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 authorsâ abstracts in the web version of this report.
The abstracts describe the researchersâ 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
Detecting grammatical errors in machine translation output using dependency parsing and treebank querying
Despite the recent advances in the field of machine translation (MT), MT systems cannot guarantee that the sentences they produce will be fluent and coherent in both syntax and semantics. Detecting and highlighting errors in machine-translated sentences can help post-editors to focus on the erroneous fragments that need to be corrected. This paper presents two methods for detecting grammatical errors in Dutch machine-translated text, using dependency parsing and treebank querying. We test our approach on the output of a statistical and a rule-based MT system for English-Dutch and evaluate the performance on sentence and word-level. The results show that our method can be used to detect grammatical errors with high accuracy on sentence-level in both types of MT output
Recommended from our members
Hybrid System Combination for Machine Translation: An Integration of Phrase-level and Sentences-level Combination Approaches
Given the wide range of successful statistical MT approaches that have emerged recently, it would be beneficial to take advantage of their individual strengths and avoid their individual weaknesses. Multi-Engine Machine Translation (MEMT) attempts to do so by either fusing the output of multiple translation engines or selecting the best translation among them, aiming to improve the overall translation quality. In this thesis, we propose to use the phrase or the sentence as our combination unit instead of the word; three new phrase-level models and one sentence-level model with novel features are proposed. This contrasts with the most popular system combination technique to date which relies on word-level confusion network decoding.
Among the three new phrase-level models, the first one utilizes source sentences and target translation hypotheses to learn hierarchical phrases -- phrases that contain subphrases (Chiang 2007). It then re-decodes the source sentences using the hierarchical phrases to combine the results of multiple MT systems. The other two models we propose view combination as a paraphrasing process and use paraphrasing rules. The paraphrasing rules are composed of either string-to-string paraphrases or hierarchical paraphrases, learned from monolingual word alignments between a selected best translation hypothesis and other hypotheses. Our experimental results show that all of the three phrase-level models give superior performance in BLEU compared with the best single translation engine. The two paraphrasing models outperform the re-decoding model and the confusion network baseline model.
The sentence-level model exploits more complex syntactic and semantic information than the phrase-level models. It uses consensus, argument alignment, a supertag-based structural language model and a syntactic error detector. We use our sentence-level model in two ways: the first selects a translated sentence from multiple MT systems as the best translation to serve as a backbone for paraphrasing process; the second makes the final decision among all fused translations generated by the phrase-level models and all translated sentences of multiple MT systems. We proposed two novel hybrid combination structures for the integration of phrase-level and sentence-level combination frameworks in order to utilize the advantages of both frameworks and provide a more diverse set of plausible fused translations to consider
A neural network architecture for detecting grammatical errors in statistical machine translation
In this paper we present a Neural Network (NN) architecture for detecting grammatical er- rors in Statistical Machine Translation (SMT) using monolingual morpho-syntactic word rep- resentations in combination with surface and syntactic context windows. We test our approach on two language pairs and two tasks, namely detecting grammatical errors and predicting over- all post-editing e ort. Our results show that this approach is not only able to accurately detect grammatical errors but it also performs well as a quality estimation system for predicting over- all post-editing e ort, which is characterised by all types of MT errors. Furthermore, we show that this approach is portable to other languages
Syntax-based machine translation using dependency grammars and discriminative machine learning
Machine translation underwent huge improvements since the groundbreaking
introduction of statistical methods in the early 2000s, going from very
domain-specific systems that still performed relatively poorly despite the
painstakingly crafting of thousands of ad-hoc rules, to general-purpose
systems automatically trained on large collections of bilingual texts which
manage to deliver understandable translations that convey the general
meaning of the original input.
These approaches however still perform quite below the level of human
translators, typically failing to convey detailed meaning and register, and
producing translations that, while readable, are often ungrammatical and
unidiomatic.
This quality gap, which is considerably large compared to most other
natural language processing tasks, has been the focus of the research in
recent years, with the development of increasingly sophisticated models that
attempt to exploit the syntactical structure of human languages, leveraging
the technology of statistical parsers, as well as advanced machine learning
methods such as marging-based structured prediction algorithms and neural
networks.
The translation software itself became more complex in order to accommodate
for the sophistication of these advanced models: the main translation
engine (the decoder) is now often combined with a pre-processor which
reorders the words of the source sentences to a target language word order, or
with a post-processor that ranks and selects a translation according according
to fine model from a list of candidate translations generated by a coarse
model.
In this thesis we investigate the statistical machine translation problem
from various angles, focusing on translation from non-analytic languages
whose syntax is best described by fluid non-projective dependency grammars
rather than the relatively strict phrase-structure grammars or projectivedependency
grammars which are most commonly used in the literature.
We propose a framework for modeling word reordering phenomena
between language pairs as transitions on non-projective source dependency
parse graphs. We quantitatively characterize reordering phenomena for the
German-to-English language pair as captured by this framework, specifically
investigating the incidence and effects of the non-projectivity of source
syntax and the non-locality of word movement w.r.t. the graph structure.
We evaluated several variants of hand-coded pre-ordering rules in order to
assess the impact of these phenomena on translation quality.
We propose a class of dependency-based source pre-ordering approaches
that reorder sentences based on a flexible models trained by SVMs and and
several recurrent neural network architectures.
We also propose a class of translation reranking models, both syntax-free
and source dependency-based, which make use of a type of neural networks
known as graph echo state networks which is highly flexible and requires
extremely little training resources, overcoming one of the main limitations
of neural network models for natural language processing tasks
Null Element Restoration
Understanding the syntactic structure of a sentence is a necessary preliminary to understanding its semantics and therefore for many practical applications. The field of natural language processing has achieved a high degree of accuracy in parsing, at least in English. However, the syntactic structures produced by the most commonly used parsers are less detailed than those structures found in the treebanks the parsers were trained on. In particular, these parsers typically lack the null elements used to indicate wh-movement, control, and other phenomena.
This thesis presents a system for inserting these null elements into parse trees in English. It then examines the problem in Arabic, which motivates a second, joint- inference system which has improved performance on English as well. Finally, it examines the application of information derived from the Google Web 1T corpus as a way of reducing certain data sparsity issues related to wh-movement
Getting Past the Language Gap: Innovations in Machine Translation
In this chapter, we will be reviewing state of the art machine translation systems, and will discuss innovative methods for machine translation, highlighting the most promising techniques and applications. Machine translation (MT) has benefited from a revitalization in the last 10 years or so, after a period of relatively slow activity. In 2005 the field received a jumpstart when a powerful complete experimental package for building MT systems from scratch became freely available as a result of the unified efforts of the MOSES international consortium. Around the same time, hierarchical methods had been introduced by Chinese researchers, which allowed the introduction and use of syntactic information in translation modeling. Furthermore, the advances in the related field of computational linguistics, making off-the-shelf taggers and parsers readily available, helped give MT an additional boost. Yet there is still more progress to be made. For example, MT will be enhanced greatly when both syntax and semantics are on board: this still presents a major challenge though many advanced research groups are currently pursuing ways to meet this challenge head-on. The next generation of MT will consist of a collection of hybrid systems. It also augurs well for the mobile environment, as we look forward to more advanced and improved technologies that enable the working of Speech-To-Speech machine translation on hand-held devices, i.e. speech recognition and speech synthesis. We review all of these developments and point out in the final section some of the most promising research avenues for the future of MT
Towards a machine-learning architecture for lexical functional grammar parsing
Data-driven grammar induction aims at producing wide-coverage grammars of human languages. Initial efforts in this field produced relatively shallow linguistic representations such as phrase-structure trees, which only encode constituent structure. Recent work on inducing deep grammars from treebanks addresses this shortcoming by also
recovering non-local dependencies and grammatical relations. My aim is to investigate the issues arising when adapting an existing Lexical Functional Grammar (LFG) induction method to a new language and treebank, and find solutions which will generalize robustly across multiple languages.
The research hypothesis is that by exploiting machine-learning algorithms to learn morphological features, lemmatization classes and grammatical functions from treebanks we can reduce the amount of manual specification and improve robustness, accuracy and domain- and language -independence for LFG parsing systems. Function labels can often be relatively straightforwardly mapped to LFG grammatical functions. Learning them reliably permits grammar induction to depend less on language-specific LFG annotation rules. I therefore propose ways to improve acquisition of function labels from treebanks and translate those improvements into better-quality f-structure parsing.
In a lexicalized grammatical formalism such as LFG a large amount of syntactically relevant information comes from lexical entries. It is, therefore, important to be able
to perform morphological analysis in an accurate and robust way for morphologically rich languages. I propose a fully data-driven supervised method to simultaneously
lemmatize and morphologically analyze text and obtain competitive or improved results on a range of typologically diverse languages
- âŠ