5,540 research outputs found
A detailed analysis of phrase-based and syntax-based machine translation: the search for systematic differences
This paper describes a range of automatic and manual comparisons of phrase-based and syntax-based statistical machine translation methods applied to English-German and
English-French translation of user-generated content. The syntax-based methods underperform the phrase-based models and the relaxation of syntactic constraints to broaden translation rule coverage means that these models do not necessarily generate output which is more grammatical than the output produced by the phrase-based models. Although the
systems generate different output and can potentially
be fruitfully combined, the lack of systematic difference between these models makes the combination task more challenging
Rank-frequency relation for Chinese characters
We show that the Zipf's law for Chinese characters perfectly holds for
sufficiently short texts (few thousand different characters). The scenario of
its validity is similar to the Zipf's law for words in short English texts. For
long Chinese texts (or for mixtures of short Chinese texts), rank-frequency
relations for Chinese characters display a two-layer, hierarchic structure that
combines a Zipfian power-law regime for frequent characters (first layer) with
an exponential-like regime for less frequent characters (second layer). For
these two layers we provide different (though related) theoretical descriptions
that include the range of low-frequency characters (hapax legomena). The
comparative analysis of rank-frequency relations for Chinese characters versus
English words illustrates the extent to which the characters play for Chinese
writers the same role as the words for those writing within alphabetical
systems.Comment: To appear in European Physical Journal B (EPJ B), 2014 (22 pages, 7
figures
Domain transfer for deep natural language generation from abstract meaning representations
Stochastic natural language generation systems that are trained from labelled datasets are often domainspecific in their annotation and in their mapping from semantic input representations to lexical-syntactic outputs. As a result, learnt models fail to generalize across domains, heavily restricting their usability beyond single applications. In this article, we focus on the problem of domain adaptation for natural language generation. We show how linguistic knowledge from a source domain, for which labelled data is available, can be adapted to a target domain by reusing training data across domains. As a key to this, we propose to employ abstract meaning representations as a common semantic representation across domains. We model natural language generation as a long short-term memory recurrent neural network encoderdecoder, in which one recurrent neural network learns a latent representation of a semantic input, and a second recurrent neural network learns to decode it to a sequence of words. We show that the learnt representations can be transferred across domains and can be leveraged effectively to improve training on new unseen domains. Experiments in three different domains and with six datasets demonstrate that the lexical-syntactic constructions learnt in one domain can be transferred to new domains and achieve up to 75-100% of the performance of in-domain training. This is based on objective metrics such as BLEU and semantic error rate and a subjective human rating study. Training a policy from prior knowledge from a different domain is consistently better than pure in-domain training by up to 10%
Handling unknown words in statistical latent-variable parsing models for Arabic, English and French
This paper presents a study of the impact of using simple and complex morphological clues to improve the classification of rare and unknown words for parsing. We compare this approach to a language-independent technique
often used in parsers which is based solely on word frequencies. This study is applied to three languages that exhibit different levels of morphological expressiveness: Arabic, French and English. We integrate information
about Arabic affixes and morphotactics into a PCFG-LA parser and obtain stateof-the-art accuracy. We also show that these morphological clues can be learnt automatically
from an annotated corpus
Exploring the effectiveness of ChatGPT-based feedback compared with teacher feedback and self-feedback: Evidence from Chinese to English translation
ChatGPT,a cutting-edge AI-powered Chatbot,can quickly generate responses on
given commands. While it was reported that ChatGPT had the capacity to deliver
useful feedback, it is still unclear about its effectiveness compared with
conventional feedback approaches,such as teacher feedback (TF) and
self-feedback (SF). To address this issue, this study compared the revised
Chinese to English translation texts produced by Chinese Master of Translation
and Interpretation (MTI) students,who learned English as a Second/Foreign
Language (ESL/EFL), based on three feedback types (i.e., ChatGPT-based
feedback, TF and SF). The data was analyzed using BLEU score to gauge the
overall translation quality as well as Coh-Metrix to examine linguistic
features across three dimensions: lexicon, syntax, and cohesion.The findings
revealed that TF- and SF-guided translation texts surpassed those with
ChatGPT-based feedback, as indicated by the BLEU score. In terms of linguistic
features,ChatGPT-based feedback demonstrated superiority, particularly in
enhancing lexical capability and referential cohesion in the translation texts.
However, TF and SF proved more effective in developing syntax-related skills,as
it addressed instances of incorrect usage of the passive voice. These diverse
outcomes indicate ChatGPT's potential as a supplementary resource,
complementing traditional teacher-led methods in translation practice
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