--- title: "Pushpak Bhattacharyya — papers, page 2 — Elamur Research" description: "Pushpak Bhattacharyya, Indian Institute of Technology Bombay: 33 papers on Elamur Research (2015–2026), h-index 51 — affiliations…" canonical: "https://research.elamur.ai/free/en/authors/pushpak-bhattacharyya/page/2/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/pushpak-bhattacharyya/page/2/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / [Pushpak Bhattacharyya](https://md.research.elamur.ai/free/en/authors/pushpak-bhattacharyya/) / page 2 # Pushpak Bhattacharyya · page 2 33 papers in our library · 2015–2026 · page 2 of 2 [The profile, affiliations and co-authors →](https://md.research.elamur.ai/free/en/authors/pushpak-bhattacharyya/) ## Papers The most cited first (OpenAlex and our library). [← prev](https://md.research.elamur.ai/free/en/authors/pushpak-bhattacharyya/) [1](https://md.research.elamur.ai/free/en/authors/pushpak-bhattacharyya/) **2** 26. **[Sarcasm in Sight and Sound: Benchmarking and Expansion to Improve Multimodal Sarcasm Detection](https://md.research.elamur.ai/free/en/papers/bhosale-2023-sarcasm-sight-sound-benchmarking-expansion/)** *(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… 27. **[Meta-Learning based Deferred Optimisation for Sentiment and Emotion aware Multi-modal Dialogue Act Classification](https://md.research.elamur.ai/free/en/papers/saha-2022-meta-learning-based-deferred-optimisation/)** *(2022 · 4 citations)* The paper asks whether knowing a speaker's sentiment and emotion helps identify the dialogue act (DA) of an utterance. 28. **[Annotated Corpus of Tweets in English from Various Domains for Emotion Detection](https://md.research.elamur.ai/free/en/papers/ghosh-2020-annotated-corpus-tweets-english-various/)** *(2020 · 4 citations)* Most emotion datasets cover a narrow set of sources, such as blogs, fairy tales, news headlines, weather or election tweets. 29. **[Computational Sarcasm](https://md.research.elamur.ai/free/en/papers/bhattacharyya-2017-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… 30. **[Post-editing a chapter of a specialized textbook into 7 languages: importance of terminological proximity with English for productivity](https://md.research.elamur.ai/free/en/papers/shah-2015-post-editing-chapter-specialized-textbook/)** *(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. 31. **[Assessing and Improving Punctuation Robustness in English-Marathi Machine Translation](https://md.research.elamur.ai/free/en/papers/shejole-2026-assessing-improving-punctuation-robustness-english/)** *(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… 32. **[How effective are VLMs in assisting humans in inferring the quality of mental models from Multimodal short answers?](https://md.research.elamur.ai/free/en/papers/sil-2026-how-effective-are-vlms-assisting/)** *(2026)* The paper asks whether vision–language models can help teachers judge how well students understand STEM concepts. 33. **[Rethinking Research on Stereotypes: An Analysis through Social Psychological and Computational Perspectives](https://md.research.elamur.ai/free/en/papers/shejole-2026-rethinking-research-stereotypes-analysis-through/)** *(2026)* This survey and position paper argues that NLP work on social harms in LLMs often treats bias as a stand-in for stereotypes. [← prev](https://md.research.elamur.ai/free/en/authors/pushpak-bhattacharyya/) [1](https://md.research.elamur.ai/free/en/authors/pushpak-bhattacharyya/) **2**