Author name cluster

Vasudeva Varma

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

2 author rows

ECAI Conference 2023 Conference Paper

XFLT: Exploring Techniques for Generating Cross Lingual Factually Grounded Long Text

Bhavyajeet Singh
Aditya Hari
Rahul Mehta 0008
Tushar Abhishek
Manish Gupta 0001
Vasudeva Varma

Multiple business scenarios require an automated generation of descriptive human-readable long text from structured input data, where the source is typically a high-resource language and the target is a low or medium resource language. We define the Cross-Lingual Fact to Long Text Generation (XFLT) as a novel natural language generation (NLG) task that involves generating descriptive and human-readable long text in a target language from structured input data (such as fact triples) in a source language. XFLT is challenging because of (a) hallucinatory nature of the state-of-the-art NLG models, (b) lack of good quality training data, and (c) lack of a suitable cross-lingual NLG metric. Unfortunately previous work focuses on different related problem settings (cross-lingual facts to short text or monolingual graph to text) and has made no efforts to handle hallucinations. In this paper, we contribute a novel dataset, XLALIGN with over 64, 000 paragraphs across 12 different languages, and English facts. We propose a novel solution to the XFLT task which addresses these challenges by training multilingual Transformer-based encoder-decoder models with coverage prompts and grounded decoding. Further, it improves on the XFLT quality by defining task-specific reward functions and training on them using reinforcement learning. On XLALIGN, we compare this novel solution with several strong baselines using a new metric, cross-lingual PARENT. We also make our code and data publicly available https: //drive. google. com/file/d/1sHgcwXKribjrm2grbs-LzXUUqXQitD2N/.

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AAAI Conference 2020 Conference Paper

Adapting Language Models for Non-Parallel Author-Stylized Rewriting

Bakhtiyar Syed
Gaurav Verma
Balaji Vasan Srinivasan
Anandhavelu Natarajan
Vasudeva Varma

Given the recent progress in language modeling using Transformer-based neural models and an active interest in generating stylized text, we present an approach to leverage the generalization capabilities of a language model to rewrite an input text in a target author’s style. Our proposed approach adapts a pre-trained language model to generate author-stylized text by ﬁne-tuning on the author-speciﬁc corpus using a denoising autoencoder (DAE) loss in a cascaded encoder-decoder framework. Optimizing over DAE loss allows our model to learn the nuances of an author’s style without relying on parallel data, which has been a severe limitation of the previous related works in this space. To evaluate the efﬁcacy of our approach, we propose a linguisticallymotivated framework to quantify stylistic alignment of the generated text to the target author at lexical, syntactic and surface levels. The evaluation framework is both interpretable as it leads to several insights about the model, and self-contained as it does not rely on external classiﬁers, e. g. sentiment or formality classiﬁers. Qualitative and quantitative assessment indicates that the proposed approach rewrites the input text with better alignment to the target style while preserving the original content better than state-of-the-art baselines.

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AAAI Conference 2018 Conference Paper

Unity in Diversity: Learning Distributed Heterogeneous Sentence Representation for Extractive Summarization

Abhishek Singh
Manish Gupta
Vasudeva Varma

Automated multi-document extractive text summarization is a widely studied research problem in the ﬁeld of natural language understanding. Such extractive mechanisms compute in some form the worthiness of a sentence to be included into the summary. While the conventional approaches rely on human crafted document-independent features to generate a summary, we develop a data-driven novel summary system called HNet, which exploits the various semantic and compositional aspects latent in a sentence to capture document independent features. The network learns sentence representation in a way that, salient sentences are closer in the vector space than non-salient sentences. This semantic and compositional feature vector is then concatenated with the documentdependent features for sentence ranking. Experiments on the DUC benchmark datasets (DUC-2001, DUC-2002 and DUC- 2004) indicate that our model shows signiﬁcant performance gain of around 1. 5-2 points in terms of ROUGE score compared with the state-of-the-art baselines.

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