Md Mosharaf Hossain


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Predicting the Focus of Negation: Model and Error Analysis
Md Mosharaf Hossain | Kathleen Hamilton | Alexis Palmer | Eduardo Blanco
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

The focus of a negation is the set of tokens intended to be negated, and a key component for revealing affirmative alternatives to negated utterances. In this paper, we experiment with neural networks to predict the focus of negation. Our main novelty is leveraging a scope detector to introduce the scope of negation as an additional input to the network. Experimental results show that doing so obtains the best results to date. Additionally, we perform a detailed error analysis providing insights into the main error categories, and analyze errors depending on whether the model takes into account scope and context information.

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It’s not a Non-Issue: Negation as a Source of Error in Machine Translation
Md Mosharaf Hossain | Antonios Anastasopoulos | Eduardo Blanco | Alexis Palmer
Findings of the Association for Computational Linguistics: EMNLP 2020

As machine translation (MT) systems progress at a rapid pace, questions of their adequacy linger. In this study we focus on negation, a universal, core property of human language that significantly affects the semantics of an utterance. We investigate whether translating negation is an issue for modern MT systems using 17 translation directions as test bed. Through thorough analysis, we find that indeed the presence of negation can significantly impact downstream quality, in some cases resulting in quality reductions of more than 60%. We also provide a linguistically motivated analysis that directly explains the majority of our findings. We release our annotations and code to replicate our analysis here:

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An Analysis of Natural Language Inference Benchmarks through the Lens of Negation
Md Mosharaf Hossain | Venelin Kovatchev | Pranoy Dutta | Tiffany Kao | Elizabeth Wei | Eduardo Blanco
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Negation is underrepresented in existing natural language inference benchmarks. Additionally, one can often ignore the few negations in existing benchmarks and still make the right inference judgments. In this paper, we present a new benchmark for natural language inference in which negation plays a critical role. We also show that state-of-the-art transformers struggle making inference judgments with the new pairs.