In simultaneous translation, the retranslation approach has the advantage of requiring no modifications to the inference engine. However, in order to reduce the undesirable flicker in the output, previous work has resorted to increasing the latency through masking, and introducing specialised inference, thus losing the simplicity of the approach. In this work, we show that self-training improves the flicker-latency tradeoff, while maintaining similar translation quality to the original. Our analysis indicates that self-training reduces flicker by controlling monotonicity. Furthermore, self-training can be combined with biased beam search to further improve the flicker-latency tradeoff.

Query-focused summaries of foreign-language, retrieved documents can help a user understand whether a document is actually relevant to the query term. A standard approach to this problem is to first translate the source documents and then perform extractive summarization to find relevant snippets. However, in a cross-lingual setting, the query term does not necessarily appear in the translations of relevant documents. In this work, we show that constrained machine translation and constrained post-editing can improve human relevance judgments by including a query term in a summary when its translation appears in the source document. We also present several strategies for selecting only certain documents for regeneration which yield further improvements

We describe our submission to the IWSLT 2021 shared task on simultaneous text-to-text English-German translation. Our system is based on the re-translation approach where the agent re-translates the whole source prefix each time it receives a new source token. This approach has the advantage of being able to use a standard neural machine translation (NMT) inference engine with beam search, however, there is a risk that incompatibility between successive re-translations will degrade the output. To improve the quality of the translations, we experiment with various approaches: we use a fixed size wait at the beginning of the sentence, we use a language model score to detect translatable units, and we apply dynamic masking to determine when the translation is unstable. We find that a combination of dynamic masking and language model score obtains the best latency-quality trade-off.

We describe the University of Edinburgh’s Bengali↔Hindi constrained systems submitted to the WMT21 News Translation task. We submitted ensembles of Transformer models built with large-scale back-translation and fine-tuned on subsets of training data retrieved based on similarity to the target domain.

In this paper, we propose a multilingual unsupervised NMT scheme which jointly trains multiple languages with a shared encoder and multiple decoders. Our approach is based on denoising autoencoding of each language and back-translating between English and multiple non-English languages. This results in a universal encoder which can encode any language participating in training into an inter-lingual representation, and language-specific decoders. Our experiments using only monolingual corpora show that multilingual unsupervised model performs better than the separately trained bilingual models achieving improvement of up to 1.48 BLEU points on WMT test sets. We also observe that even if we do not train the network for all possible translation directions, the network is still able to translate in a many-to-many fashion leveraging encoder’s ability to generate interlingual representation.

We describe our submission to WMT 2019 News translation shared task for Gujarati-English language pair. We submit constrained systems, i.e, we rely on the data provided for this language pair and do not use any external data. We train Transformer based subword-level neural machine translation (NMT) system using original parallel corpus along with synthetic parallel corpus obtained through back-translation of monolingual data. Our primary systems achieve BLEU scores of 10.4 and 8.1 for Gujarati→English and English→Gujarati, respectively. We observe that incorporating monolingual data through back-translation improves the BLEU score significantly over baseline NMT and SMT systems for this language pair.

In this paper, we describe the IIT Patna’s submission to WMT 2019 shared task on parallel corpus filtering. This shared task asks the participants to develop methods for scoring each parallel sentence from a given noisy parallel corpus. Quality of the scoring method is judged based on the quality of SMT and NMT systems trained on smaller set of high-quality parallel sentences sub-sampled from the original noisy corpus. This task has two language pairs. We submit for both the Nepali-English and Sinhala-English language pairs. We define fuzzy string matching score between English and the translated (into English) source based on Levenshtein distance. Based on the scores, we sub-sample two sets (having 1 million and 5 millions English tokens) of parallel sentences from each parallel corpus, and train SMT systems for development purpose only. The organizers publish the official evaluation using both SMT and NMT on the final official test set. Total 10 teams participated in the shared task and according the official evaluation, our scoring method obtains 2nd position in the team ranking for 1-million NepaliEnglish NMT and 5-million Sinhala-English NMT categories.

Efficient word representations play an important role in solving various problems related to Natural Language Processing (NLP), data mining, text mining etc. The issue of data sparsity poses a great challenge in creating efficient word representation model for solving the underlying problem. The problem is more intensified in resource-poor scenario due to the absence of sufficient amount of corpus. In this work we propose to minimize the effect of data sparsity by leveraging bilingual word embeddings learned through a parallel corpus. We train and evaluate Long Short Term Memory (LSTM) based architecture for aspect level sentiment classification. The neural network architecture is further assisted by the hand-crafted features for the prediction. We show the efficacy of the proposed model against state-of-the-art methods in two experimental setups i.e. multi-lingual and cross-lingual.

In this paper we describe the system that we develop as part of our participation in WAT 2016. We develop a system based on hierarchical phrase-based SMT for English to Hindi language pair. We perform re-ordering and augment bilingual dictionary to improve the performance. As a baseline we use a phrase-based SMT model. The MT models are fine-tuned on the development set, and the best configurations are used to report the evaluation on the test set. Experiments show the BLEU of 13.71 on the benchmark test data. This is better compared to the official baseline BLEU score of 10.79.