Pushpak Bhattacharyya · page 2
Papers
- Sarcasm in Sight and Sound: Benchmarking and Expansion to Improve Multimodal Sarcasm Detection (2023 · 4 citations)
Sarcasm often cannot be recognized from words alone. Tone of voice and facial expression may be what turns a sentence ironic. This paper revisits MUStARD++, a sarcasm dataset of TV-show clips with text, audio and video. It makes two contributions. First, it re-benchmarks the dataset with modern pretrained encoders in… - Meta-Learning based Deferred Optimisation for Sentiment and Emotion aware Multi-modal Dialogue Act Classification (2022 · 4 citations)
The paper asks whether knowing a speaker's sentiment and emotion helps identify the dialogue act (DA) of an utterance. - Annotated Corpus of Tweets in English from Various Domains for Emotion Detection (2020 · 4 citations)
Most emotion datasets cover a narrow set of sources, such as blogs, fairy tales, news headlines, weather or election tweets. - Computational Sarcasm (2017 · 1 citation)
This is a tutorial abstract from EMNLP 2017, not a research paper with experiments. It outlines a three-hour course on computational sarcasm, meaning automatic methods for handling sarcastic text. The motivation is that sarcasm undermines sentiment analysis, because the literal polarity of a sarcastic utterance often… - Post-editing a chapter of a specialized textbook into 7 languages: importance of terminological proximity with English for productivity (2015 · 1 citation)
The paper asks whether foreign students can turn free machine translations of specialized textbooks into usable versions in their native languages by post-editing them cooperatively. - Assessing and Improving Punctuation Robustness in English-Marathi Machine Translation (2026)
Neural MT systems often depend on punctuation to work out sentence structure. User-written text frequently lacks commas or uses them wrongly, which can yield Marathi output that reads smoothly but means something else. The authors study this weakness for English→Marathi. They built Virām, a small hand-curated… - How effective are VLMs in assisting humans in inferring the quality of mental models from Multimodal short answers? (2026)
The paper asks whether vision–language models can help teachers judge how well students understand STEM concepts. - Rethinking Research on Stereotypes: An Analysis through Social Psychological and Computational Perspectives (2026)
This survey and position paper argues that NLP work on social harms in LLMs often treats bias as a stand-in for stereotypes.
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