Ratish Puduppully


2022

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IndicBART: A Pre-trained Model for Indic Natural Language Generation
Raj Dabre | Himani Shrotriya | Anoop Kunchukuttan | Ratish Puduppully | Mitesh Khapra | Pratyush Kumar
Findings of the Association for Computational Linguistics: ACL 2022

In this paper, we study pre-trained sequence-to-sequence models for a group of related languages, with a focus on Indic languages. We present IndicBART, a multilingual, sequence-to-sequence pre-trained model focusing on 11 Indic languages and English. IndicBART utilizes the orthographic similarity between Indic scripts to improve transfer learning between similar Indic languages. We evaluate IndicBART on two NLG tasks: Neural Machine Translation (NMT) and extreme summarization. Our experiments on NMT and extreme summarization show that a model specific to related languages like IndicBART is competitive with large pre-trained models like mBART50 despite being significantly smaller. It also performs well on very low-resource translation scenarios where languages are not included in pre-training or fine-tuning. Script sharing, multilingual training, and better utilization of limited model capacity contribute to the good performance of the compact IndicBART model.

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Data-to-text Generation with Variational Sequential Planning
Ratish Puduppully | Yao Fu | Mirella Lapata
Transactions of the Association for Computational Linguistics, Volume 10

We consider the task of data-to-text generation, which aims to create textual output from non-linguistic input. We focus on generating long-form text, that is, documents with multiple paragraphs, and propose a neural model enhanced with a planning component responsible for organizing high-level information in a coherent and meaningful way. We infer latent plans sequentially with a structured variational model, while interleaving the steps of planning and generation. Text is generated by conditioning on previous variational decisions and previously generated text. Experiments on two data-to-text benchmarks (RotoWire and MLB) show that our model outperforms strong baselines and is sample-efficient in the face of limited training data (e.g., a few hundred instances).

2021

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Data-to-text Generation with Macro Planning
Ratish Puduppully | Mirella Lapata
Transactions of the Association for Computational Linguistics, Volume 9

Abstract Recent approaches to data-to-text generation have adopted the very successful encoder-decoder architecture or variants thereof. These models generate text that is fluent (but often imprecise) and perform quite poorly at selecting appropriate content and ordering it coherently. To overcome some of these issues, we propose a neural model with a macro planning stage followed by a generation stage reminiscent of traditional methods which embrace separate modules for planning and surface realization. Macro plans represent high level organization of important content such as entities, events, and their interactions; they are learned from data and given as input to the generator. Extensive experiments on two data-to-text benchmarks (RotoWire and MLB) show that our approach outperforms competitive baselines in terms of automatic and human evaluation.

2019

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Data-to-text Generation with Entity Modeling
Ratish Puduppully | Li Dong | Mirella Lapata
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Recent approaches to data-to-text generation have shown great promise thanks to the use of large-scale datasets and the application of neural network architectures which are trained end-to-end. These models rely on representation learning to select content appropriately, structure it coherently, and verbalize it grammatically, treating entities as nothing more than vocabulary tokens. In this work we propose an entity-centric neural architecture for data-to-text generation. Our model creates entity-specific representations which are dynamically updated. Text is generated conditioned on the data input and entity memory representations using hierarchical attention at each time step. We present experiments on the RotoWire benchmark and a (five times larger) new dataset on the baseball domain which we create. Our results show that the proposed model outperforms competitive baselines in automatic and human evaluation.

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University of Edinburgh’s submission to the Document-level Generation and Translation Shared Task
Ratish Puduppully | Jonathan Mallinson | Mirella Lapata
Proceedings of the 3rd Workshop on Neural Generation and Translation

The University of Edinburgh participated in all six tracks: NLG, MT, and MT+NLG with both English and German as targeted languages. For the NLG track, we submitted a multilingual system based on the Content Selection and Planning model of Puduppully et al (2019). For the MT track, we submitted Transformer-based Neural Machine Translation models, where out-of-domain parallel data was augmented with in-domain data extracted from monolingual corpora. Our MT+NLG systems disregard the structured input data and instead rely exclusively on the source summaries.

2017

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Transition-Based Deep Input Linearization
Ratish Puduppully | Yue Zhang | Manish Shrivastava
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

Traditional methods for deep NLG adopt pipeline approaches comprising stages such as constructing syntactic input, predicting function words, linearizing the syntactic input and generating the surface forms. Though easier to visualize, pipeline approaches suffer from error propagation. In addition, information available across modules cannot be leveraged by all modules. We construct a transition-based model to jointly perform linearization, function word prediction and morphological generation, which considerably improves upon the accuracy compared to a pipelined baseline system. On a standard deep input linearization shared task, our system achieves the best results reported so far.

2016

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Transition-Based Syntactic Linearization with Lookahead Features
Ratish Puduppully | Yue Zhang | Manish Shrivastava
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2015

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Brahmi-Net: A transliteration and script conversion system for languages of the Indian subcontinent
Anoop Kunchukuttan | Ratish Puduppully | Pushpak Bhattacharyya
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations

2014

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Merging Verb Senses of Hindi WordNet using Word Embeddings
Sudha Bhingardive | Ratish Puduppully | Dhirendra Singh | Pushpak Bhattacharyya
Proceedings of the 11th International Conference on Natural Language Processing