Token and Type Constraints for Cross-Lingual Part-of-Speech Tagging

We consider the construction of part-of-speech taggers for resource-poor languages. Recently, manually constructed tag dictionaries from Wiktionary and dictionaries projected via bitext have been used as type constraints to overcome the scarcity of annotated data in this setting. In this paper, we show that additional token constraints can be projected from a resource-rich source language to a resource-poor target language via word-aligned bitext. We present several models to this end; in particular a partially observed conditional random field model, where coupled token and type constraints provide a partial signal for training. Averaged across eight previously studied Indo-European languages, our model achieves a 25% relative error reduction over the prior state of the art. We further present successful results on seven additional languages from different families, empirically demonstrating the applicability of coupled token and type constraints across a diverse set of languages

Integrated Parallel Sentence and Fragment Extraction from Comparable Corpora: A Case Study on Chinese--Japanese Wikipedia

Parallel corpora are crucial for statistical machine translation (SMT); however, they are quite scarce for most language pairs and domains. As comparable corpora are far more available, many studies have been conducted to extract either parallel sentences or fragments from them for SMT. In this article, we propose an integrated system to extract both parallel sentences and fragments from comparable corpora. We first apply parallel sentence extraction to identify parallel sentences from comparable sentences. We then extract parallel fragments from the comparable sentences. Parallel sentence extraction is based on a parallel sentence candidate filter and classifier for parallel sentence identification. We improve it by proposing a novel filtering strategy and three novel feature sets for classification. Previous studies have found it difficult to accurately extract parallel fragments from comparable sentences. We propose an accurate parallel fragment extraction method that uses an alignment model to locate the parallel fragment candidates and an accurate lexicon-based filter to identify the truly parallel fragments. A case study on the Chinese--Japanese Wikipedia indicates that our proposed methods outperform previously proposed methods, and the parallel data extracted by our system significantly improves SMT performance

Speed-to-Quality Ratio in Fully Human Translation vs. Post-Editing of Machine Translation Output

Bearing in mind an ever-growing amount of texts to be translated, often under very tight deadlines, there is growing pressure in the industry to use translation technology to speed up the translation process. More and more often, translators are called upon to post-edit machine translation (MT) output rather than translate a text from the very beginning. The primary purpose of this text is to examine whether post-editing of MT output indeed leads to greater speed and quality in translation. This study compares the speed and quality of fully human translations to those of post-edited MT output for three different text types: a novel, a news report, and a legal text. Quality is assessed along four parameters, namely the morphosyntactic features, the semantic features, style, and general impression. The MT system used for the purposes of the study is Google Translate

Deep Learning for Text Style Transfer: A Survey

Text style transfer is an important task in natural language generation, which aims to control certain attributes in the generated text, such as politeness, emotion, humor, and many others. It has a long history in the field of natural language processing, and recently has re-gained significant attention thanks to the promising performance brought by deep neural models. In this paper, we present a systematic survey of the research on neural text style transfer, spanning over 100 representative articles since the first neural text style transfer work in 2017. We discuss the task formulation, existing datasets and subtasks, evaluation, as well as the rich methodologies in the presence of parallel and non-parallel data. We also provide discussions on a variety of important topics regarding the future development of this task. Our curated paper list is at https://github.com/zhijing-jin/Text_Style_Transfer_SurveyComment: Computational Linguistics Journal 202

Sentence Similarity and Machine Translation

Neural machine translation (NMT) systems encode an input sentence into an intermediate representation and then decode that representation into the output sentence. Translation requires deep understanding of language; as a result, NMT models trained on large amounts of data develop a semantically rich intermediate representation. We leverage this rich intermediate representation of NMT systems—in particular, multilingual NMT systems, which learn to map many languages into and out of a joint space—for bitext curation, paraphrasing, and automatic machine translation (MT) evaluation. At a high level, all of these tasks are rooted in similarity: sentence and document alignment requires measuring similarity of sentences and documents, respectively; paraphrasing requires producing output which is similar to an input; and automatic MT evaluation requires measuring the similarity between MT system outputs and corresponding human reference translations. We use multilingual NMT for similarity in two ways: First, we use a multilingual NMT model with a fixed-size intermediate representation (Artetxe and Schwenk, 2018) to produce multilingual sentence embeddings, which we use in both sentence and document alignment. Second, we train a multilingual NMT model and show that it generalizes to the task of generative paraphrasing (i.e., “translating” from Russian to Russian), when used in conjunction with a simple generation algorithm to discourage copying from the input to the output. We also use this model for automatic MT evaluation, to force decode and score MT system outputs conditioned on their respective human reference translations. Since we leverage multilingual NMT models, each method works in many languages using a single model. We show that simple methods, which leverage the intermediate representation of multilingual NMT models trained on large amounts of bitext, outperform prior work in paraphrasing, sentence alignment, document alignment, and automatic MT evaluation. This finding is consistent with recent trends in the natural language processing community, where large language models trained on huge amounts of unlabeled text have achieved state-of-the-art results on tasks such as question answering, named entity recognition, and parsing