Korean to English Translation Using Synchronous TAGs

It is often argued that accurate machine translation requires reference to contextual knowledge for the correct treatment of linguistic phenomena such as dropped arguments and accurate lexical selection. One of the historical arguments in favor of the interlingua approach has been that, since it revolves around a deep semantic representation, it is better able to handle the types of linguistic phenomena that are seen as requiring a knowledge-based approach. In this paper we present an alternative approach, exemplified by a prototype system for machine translation of English and Korean which is implemented in Synchronous TAGs. This approach is essentially transfer based, and uses semantic feature unification for accurate lexical selection of polysemous verbs. The same semantic features, when combined with a discourse model which stores previously mentioned entities, can also be used for the recovery of topicalized arguments. In this paper we concentrate on the translation of Korean to English.Comment: ps file. 8 page

Does orthographic overlap influence lexical selection?

Understanding whether and how various processes interact in language production can help us both understand aphasic errors and develop theoretically motivated treatment approaches. We examined semantic errors produced in writing-to-dictation by an individual with acquired dysgraphia to determine whether letter-level information – particularly overlap between the target and the semantic error – can affect lexical selection processes in these errors. Our results indicated that the particular semantic errors that were produced were significantly more likely to share orthographic structure than would be expected by chance alone, indicating interaction in the form of feedback from letter-level processes to lexical selection

A syntactic skeleton for statistical machine translation

We present a method for improving statistical machine translation performance by using linguistically motivated syntactic information. Our algorithm recursively decomposes source language sentences into syntactically simpler and shorter chunks, and recomposes their translation to form target language sentences. This improves both the word order and lexical selection of the translation. We report statistically significant relative improvementsof 3.3% BLEU score in an experiment (English!Spanish) carried out on an 800-sentence test set extracted from the Europarl corpus

Lexical Selection in Multi-Word Production

In multi-word utterances, target words need to be selected in the context of other target words. In the present study, three hypotheses were tested that differed in their assumptions about whether the lexical selection mechanism considers the activation levels of the other target lexical representations, and whether it takes into account their grammatical class properties. Participants produced adjective + noun and noun + noun utterances in response to colored word and picture + word stimulus displays. In both types of utterances, the frequency of the first and second response was manipulated. The results revealed an effect of the frequency of the second response that did not depend on the utterance type, and additive effects for the frequency of the first and the second response in both utterance types. These results are interpreted in terms of a model of lexical selection that assumes that selection is non-competitive

Bilingual lexical selection as a dynamic process:evidence from Arabic-French bilinguals

The nature of the lexical selection process in bilingual spoken word production is one of the pending questions of research on bilingualism. According to one view this competitive process is language-specific, while another holds that it is language-nonspecific (i.e., lexical competition is cross-linguistic). In recent years, research on bilingual language production has seen the rise of a third view that postulates that lexical selection is in fact dynamic and may function as language-specific or nonspecific depending on a number of factors. The aim of the present study was to investigate the lexical selection process among moderately proficient bilinguals whose two languages are typologically distant: Tunisian Arabic and French. The picture-word interference task was used in two experiments where moderately proficient Tunisian Arabic (L1)-French (L2) bilinguals were asked to name pictures in their L2 while ignoring auditory distractors (semantic, phono-translation, phonological, or unrelated) in their L2 (Experiment 1) or their L1 (Experiment 2). Thus, the language context was entirely monolingual in Experiment 1 and bilingual in Experiment 2. In Experiment 1, only a phonological facilitation effect was observed. In Experiment 2, interference was found in the phono-translation, semantic, and phonological conditions. Taken together, these results indicate that cross-language competition occurs among moderately proficient Tunisian Arabic-French bilinguals only in a bilingual context (Experiment 2) as indexed by the phono-translation interference effect observed. Our findings are in line with the recent hypothesis that lexical selection is a dynamic process modulated by factors like language similarity, language proficiency, and the experimental language context

Lexical selection and the evolution of language units

In this paper I discuss similarities and differences between a potential new model of language development - lexical selection, and its biological equivalent - natural selection. Based on Dawkins' (1976) concept of the meme I discuss two units of language and explore their potential to be seen as linguistic replicators. The central discussion revolves around two key parts - the units that could potentially play the role of replicators in a lexical selection system and a visual representation of the model proposed. draw on work by Hoey (2005), Wray (2008) and Sinclair (1996, 1998) for the theoretical basis; Croft (2000) is highlighted as a similar framework. Finally brief examples are taken from the free online corpora provided by the corpus analysis tool Sketch Engine (Kilgarriff, Rychly, Smrz and Tugwell 2004) to ground the discussion in real world communicative situations. The examples highlight the point that different situational contexts will allow for different units to flourish based on the local social and linguistic environment. The paper also shows how a close look at the specific context and strings available to a language user at any given moment has potential to illuminate different aspects of language when compared with a more abstract approach