Quality Estimation (QE) is an important component of the machine translation workflow as it assesses the quality of the translated output without consulting reference translations. In this paper, we discuss our submission to the WMT 2021 QE Shared Task. We participate in Task 2 sentence-level sub-task that challenge participants to predict the HTER score for sentence-level post-editing effort. Our proposed system is an ensemble of multilingual BERT (mBERT)-based regression models, which are generated by fine-tuning on different input settings. It demonstrates comparable performance with respect to the Pearson's correlation, and beat the baseline system in MAE/ RMSE for several language pairs. In addition, we adapt our system for the zero-shot setting by exploiting target language-relevant language pairs and pseudo-reference translations.

Ensemble Fine-tuned mBERT for Translation Quality Estimation

Domain knowledge is important to understand
both the lexical and relational associations of
words in natural language text, especially for
domain-specific tasks like Natural Language
Inference (NLI) in the medical domain, where
due to the lack of a large annotated dataset
such knowledge cannot be implicitly learned
during training. However, because of the linguistic
idiosyncrasies of clinical texts (e.g.,
shorthand jargon), solely relying on domain
knowledge from an external knowledge base
(e.g., UMLS) can lead to wrong inference predictions
as it disregards contextual information
and, hence, does not return the most relevant
mapping. To remedy this, we devise
a knowledge adaptive approach for medical
NLI that encodes the premise/hypothesis texts
by leveraging supplementary external knowledge,
alongside the UMLS, based on the word
contexts. By incorporating refined domain
knowledge at both the lexical and relational levels
through a multi-source attention mechanism,
it is able to align the token-level interactions
between the premise and hypothesis more
effectively. Comprehensive experiments and
case study on the recently released MedNLI
dataset are conducted to validate the effectiveness
of the proposed approach.

Improving Medical NLI Using Context-Aware
Domain Knowledge



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W11: Sixth Conference on Machine Translation Day 1

workshop paper

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible. The experience for on-site participants would closely approximate a normal pre-COVID *ACL conference, with 5-6 thematically organized parallel sessions and live Q/A and interactive discussion immediately after the talks. Presentations by virtual participants will be equitably interleaved with those of on-site participants, projected on the auditorium screens as if on-site, and also followed immediately by live Q/A and interactive discussion at a time during reasonable waking hours for the virtual presenter. For all participants, on-site and virtual, who are unable to attend a session due to either time-zone issues or because they are participating in another session live, talk recordings and slides will be available online at a minimum after the live presentation (and in many cases before as well), and questions may be submitted in advance on session-specific discussion boards and answered live in session with the usual visual aids if desired.

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EMNLP 2021

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible.

9th Joint Conference on Lexical and Computational Semantics (*SEM)

technical paper

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes, but also in industrial research departments and in technological startups. COLING conferences provide opportunities for all these communities to showcase their exciting developments.

COLING 2020

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

Shaika Chowdhury

2

1

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Ensemble Fine-tuned mBERT for Translation Quality Estimation

Improving Medical NLI Using Context-Aware Domain Knowledge

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