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Arnab Bhattacharya

ACL 2026×2数据与预训练多语言与语言学对齐与偏好推理可解释性
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0 一作 · 2 篇 · ACL 2026

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2026 顶会论文

2
作者位次 #3/3ACL 2026Poster· Natural Language Processing

Paramanu: Compact and Competitive Monolingual Language Models for Low-Resource Morphologically Rich I ndian Languages

Mitodru Niyogi · Eric Gaussier · Arnab Bhattacharya

摘要

Multilingual large language models (LLMs) are expensive to pretrain and often suffer from imbalances across languages and datasets, English-centric bias, tokenizer oversegmentation for morphologically rich low-resource languages, and the curse of multilinguality. We introduce PARAMANU, a family o...

数据与预训练多语言与语言学对齐与偏好OpenReview 项目页
作者位次 #3/3ACL 2026Workshop· Natural Language Processing

Thesis Proposal: A Normalization-First Framework for Sound, Complete, and Utility-Ready Open Information Extraction

Chandan Prakash · Pavan Kumar Chittimalli · Arnab Bhattacharya

摘要

Open Information Extraction (OIE) has largely focused on extracting relational tuples from text, yet in its current form remains unsuitable for downstream systems due to the absence of standardized, semantically sound representations. This thesis argues that the field has been addressing extracti...

推理可解释性评测与基准OpenReview 项目页

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