Domain-specific Neural Machine Translation (NMT) model can provide improved performance, however, it is difficult to always access a domain-specific parallel corpus. Iterative Back-Translation can be used for fine-tuning an NMT model for a domain even if only a monolingual domain corpus is available. The quality of synthetic parallel corpora in terms of closeness to in-domain sentences can play an important role in the performance of the translation model. Recent works have shown that filtering at different stages of the back translation and weighting the sentences can provide state-of-the-art performance. In comparison, in this work, we observe that a simpler filtering approach based on a domain classifier, applied only to the pseudo-training data can consistently perform better, providing performance gains of 1.40, 1.82 and 0.76 in terms of BLEU score for Medical, Law and IT in one direction, and 1.28, 1.60 and 1.60 in the other direction in low resource scenario over competitive baselines. In the high resource scenario, our approach is at par with competitive baselines.

Social media platforms and discussion forums such as Reddit, Twitter, etc. are filled with figurative languages. Sarcasm is one such category of figurative language whose presence in a conversation makes language understanding a challenging task. In this paper, we present a deep neural architecture for sarcasm detection. We investigate various pre-trained language representation models (PLRMs) like BERT, RoBERTa, etc. and fine-tune it on the Twitter dataset. We experiment with a variety of PLRMs either on the twitter utterance in isolation or utilizing the contextual information along with the utterance. Our findings indicate that by taking into consideration the previous three most recent utterances, the model is more accurately able to classify a conversation as being sarcastic or not. Our best performing ensemble model achieves an overall F1 score of 0.790, which ranks us second on the leaderboard of the Sarcasm Shared Task 2020.

Document-Level Machine Translation (MT) has become an active research area among the NLP community in recent years. Unlike sentence-level MT, which translates the sentences independently, document-level MT aims to utilize contextual information while translating a given source sentence. This paper demonstrates our submission (Team ID - DEEPNLP) to the Document-Level Translation task organized by WAT 2020. This task focuses on translating texts from a business dialog corpus while optionally utilizing the context present in the dialog. In our proposed approach, we utilize publicly available parallel corpus from different domains to train an open domain base NMT model. We then use monolingual target data to create filtered pseudo parallel data and employ Back-Translation to fine-tune the base model. This is further followed by fine-tuning on the domain-specific corpus. We also ensemble various models to improvise the translation performance. Our best models achieve a BLEU score of 26.59 and 22.83 in an unconstrained setting and 15.10 and 10.91 in the constrained settings for En->Ja & Ja->En direction, respectively.