Search Results for author: Mohaddeseh Bastan

Found 6 papers, 4 papers with code

BioNLI: Generating a Biomedical NLI Dataset Using Lexico-semantic Constraints for Adversarial Examples

1 code implementation26 Oct 2022 Mohaddeseh Bastan, Mihai Surdeanu, Niranjan Balasubramanian

We introduce a novel semi-supervised procedure that bootstraps an NLI dataset from existing biomedical dataset that pairs mechanisms with experimental evidence in abstracts.

Decision Making Natural Language Inference

SuMe: A Dataset Towards Summarizing Biomedical Mechanisms

2 code implementations ACL ARR November 2021 Mohaddeseh Bastan, Nishant Shankar, Mihai Surdeanu, Niranjan Balasubramanian

We leverage this structure and create a summarization task, where the input is a collection of sentences and the main entities in an abstract, and the output includes the relationship and a sentence that summarizes the mechanism.


A Preordered RNN Layer Boosts Neural Machine Translation in Low Resource Settings

no code implementations loresmt (COLING) 2022 Mohaddeseh Bastan, Shahram Khadivi

Neural Machine Translation (NMT) models are strong enough to convey semantic and syntactic information from the source language to the target language.

Machine Translation NMT +1

Author's Sentiment Prediction

1 code implementation COLING 2020 Mohaddeseh Bastan, Mahnaz Koupaee, Youngseo Son, Richard Sicoli, Niranjan Balasubramanian

We introduce PerSenT, a dataset of crowd-sourced annotations of the sentiment expressed by the authors towards the main entities in news articles.

Sentiment Analysis

Modeling Label Semantics for Predicting Emotional Reactions

1 code implementation ACL 2020 Radhika Gaonkar, Heeyoung Kwon, Mohaddeseh Bastan, Niranjan Balasubramanian, Nathanael Chambers

Predicting how events induce emotions in the characters of a story is typically seen as a standard multi-label classification task, which usually treats labels as anonymous classes to predict.

Emotion Classification Multi-Label Classification

Neural Machine Translation on Scarce-Resource Condition: A case-study on Persian-English

no code implementations7 Jan 2017 Mohaddeseh Bastan, Shahram Khadivi, Mohammad Mehdi Homayounpour

This new loss function yields a total of 1. 87 point improvements in terms of BLEU score in the translation quality.

NMT Translation +2

Cannot find the paper you are looking for? You can Submit a new open access paper.