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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
Adapting End-to-End Speech Recognition for Readable Subtitles
Automatic speech recognition (ASR) systems are primarily evaluated on
transcription accuracy. However, in some use cases such as subtitling, verbatim
transcription would reduce output readability given limited screen size and
reading time. Therefore, this work focuses on ASR with output compression, a
task challenging for supervised approaches due to the scarcity of training
data. We first investigate a cascaded system, where an unsupervised compression
model is used to post-edit the transcribed speech. We then compare several
methods of end-to-end speech recognition under output length constraints. The
experiments show that with limited data far less than needed for training a
model from scratch, we can adapt a Transformer-based ASR model to incorporate
both transcription and compression capabilities. Furthermore, the best
performance in terms of WER and ROUGE scores is achieved by explicitly modeling
the length constraints within the end-to-end ASR system.Comment: IWSLT 202
A Survey of Paraphrasing and Textual Entailment Methods
Paraphrasing methods recognize, generate, or extract phrases, sentences, or
longer natural language expressions that convey almost the same information.
Textual entailment methods, on the other hand, recognize, generate, or extract
pairs of natural language expressions, such that a human who reads (and trusts)
the first element of a pair would most likely infer that the other element is
also true. Paraphrasing can be seen as bidirectional textual entailment and
methods from the two areas are often similar. Both kinds of methods are useful,
at least in principle, in a wide range of natural language processing
applications, including question answering, summarization, text generation, and
machine translation. We summarize key ideas from the two areas by considering
in turn recognition, generation, and extraction methods, also pointing to
prominent articles and resources.Comment: Technical Report, Natural Language Processing Group, Department of
Informatics, Athens University of Economics and Business, Greece, 201
Multilingual Unsupervised Sentence Simplification
Progress in Sentence Simplification has been hindered by the lack of
supervised data, particularly in languages other than English. Previous work
has aligned sentences from original and simplified corpora such as English
Wikipedia and Simple English Wikipedia, but this limits corpus size, domain,
and language. In this work, we propose using unsupervised mining techniques to
automatically create training corpora for simplification in multiple languages
from raw Common Crawl web data. When coupled with a controllable generation
mechanism that can flexibly adjust attributes such as length and lexical
complexity, these mined paraphrase corpora can be used to train simplification
systems in any language. We further incorporate multilingual unsupervised
pretraining methods to create even stronger models and show that by training on
mined data rather than supervised corpora, we outperform the previous best
results. We evaluate our approach on English, French, and Spanish
simplification benchmarks and reach state-of-the-art performance with a totally
unsupervised approach. We will release our models and code to mine the data in
any language included in Common Crawl
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