Shashi Narayan
I am a Senior Research Scientist at Google London. I am part of the language understanding team focusing on natural language generation. Before joining Google, I was part of the EdinburghNLP group, working with Shay Cohen and Mirella Lapata. I did my PhD at Université de Lorraine, Loria INRIA, Nancy with Claire Gardent. Prior to this, I was awarded Erasmus Mundus Masters scholarship in Language and Communication Technology (EM-LCT). I did my major (Bachelor of Technology, Honors) in Computer Science and Engineering from Indian Institute of Technology (IIT), Kharagpur India. I was a member of Super 30.
More info on my home page.
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Text-Blueprint: An Interactive Platform for Plan-based Conditional Generation
Fantine Huot
Reinald Kim Amplayo
Mirella Lapata
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations (2023)
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While conditional generation models can now generate natural language well enough to create fluent text, it is still difficult to control the generation process, leading to irrelevant, repetitive, and hallucinated content. Recent work shows that planning can be a useful intermediate step to render conditional generation less opaque and more grounded. We present a web browser-based demonstration for query-focused summarization that uses a sequence of question-answer pairs, as a blueprint plan for guiding text generation (i.e., what to say and in what order). We illustrate how users may interact with the generated text and associated plan visualizations, e.g., by editing and modifying the blueprint in order to improve or control the generated output.
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Conditional Generation with a Question-Answering Blueprint
Reinald Kim Amplayo
Fantine Huot
Mirella Lapata
Transactions of the Association for Computational Linguistics (2023) (to appear)
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The ability to convey relevant and faithful information is critical for many tasks in conditional generation and yet remains elusive for neural seq-to-seq models whose outputs often reveal hallucinations and fail to correctly cover important details. In this work, we advocate planning as a useful intermediate representation for rendering conditional generation less opaque and more grounded. Our work proposes a new conceptualization of text plans as a sequence of question-answer (QA) pairs. We enhance existing datasets (e.g., for summarization) with a QA blueprint operating as a proxy for both content selection (i.e., what to say) and planning (i.e., in what order). We obtain blueprints automatically by exploiting state-of-the-art question generation technology and convert input-output pairs into input-blueprint-output tuples. We develop Transformer-based models, each varying in how they incorporate the blueprint in the generated output (e.g., as a global plan or iteratively). Evaluation across metrics and datasets demonstrates that blueprint models are more factual than alternatives which do not resort to planning and allow tighter control of the generation output.
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Query Refinement Prompts for Closed-Book Long-Form Question Answering
Reinald Kim Amplayo
arXiv submission (2022)
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Large language models (LLMs) have been shown to perform well in answering questions and in producing long-form texts such as stories and explanations, both in few-shot closed-book settings. While the former can be validated using well-known evaluation metrics, the latter is difficult to evaluate. To this end, we investigate the ability of LLMs to do both tasks at once -- to do question answering that requires long-form answers. Such questions tend to be multifaceted, i.e., they may have ambiguities and/or require information from multiple sources. To this end, we define query refinement prompts that encourage LLMs to explicitly express the multifacetedness in questions and generate long-form answers covering multiple facets of the question. Our experiments on two long-form question answering datasets, ASQA and AQuAMuSe, show that using our prompts allows us to outperform fully finetuned models in the closed book setting, as well as achieve results comparable to retrieve-then-generate open-book models.
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A Well-Composed Text is Half Done! Composition Sampling for Diverse Conditional Generation
Yao Zhao
Mirella Lapata
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL 2022), Association for Computational Linguistics, pp. 21
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We propose Composition Sampling, a simple but effective method to generate diverse outputs for conditional generation of higher quality compared to previous stochastic decoding strategies. It builds on recently proposed plan-based neural generation models (Narayan et al., 2021) that are trained to first create a composition of the output and then generate by conditioning on it and the input. Our approach
avoids text degeneration by first sampling a composition in the form of an entity chain and then using beam search to generate the best possible text grounded to this entity chain. Experiments on summarization (CNN/DailyMail and XSum) and question generation (SQuAD), using existing and newly proposed automatic metrics together with human-based evaluation, demonstrate that Composition Sampling is currently the best available decoding strategy for generating diverse meaningful outputs.
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In this paper we introduce a Focus Attention MEchanism to two popular Seq2Seq architectures: RoBERTaS2S and Pegasus . Both RoBERTaS2S and Pegasus use Transformer-based encoder-decoder architecture; at each decoding step decoder learns a single contextual representation necessary to predict the next token by attending to the input sequence and the sequence that has been predicted so far. The focus attention takes inspiration from human-written text and augments this contextual representation through a dynamic vocabulary biasing to proactively generate tokens that are similar or topical to the input sequence. When evaluated on the BBC extreme summarization task, both RoBERTaS2S and Pegasus with Focus Attention generate summaries that are more faithful to their input documents, in comparison to their counterparts. Models with focus attention can holistically learn any abstract-level properties, such as mostly extractive, mostly abstractive or text-editing only, embodied in the target texts, without introducing any task-specific architectural priors. Finally, by its virtue, it supports Focus Sampling -- a technique to sample topically relevant tokens to generate diverse, yet topically consistent and faithful outputs.
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Planning with Learned Entity Prompts for Abstractive Summarization
Yao Zhao
Ryan McDonald
Transactions of the Association for Computational Linguistics, vol. 9 (2021), 1475–1492
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We investigate Entity Chain -- a chain of related entities in the summary -- as an intermediate summary representation to better plan and ground the generation of abstractive summaries. In particular, we achieve this by augmenting the target by appending it with an entity chain extracted from the target. We experiment with Transformer-based encoder-decoder models; a transformer encoder first encodes the input and a transformer decoder generates an intermediate summary representation in the form of an entity chain and then continues generating the summary conditioned on the entity chain and the input. We evaluate our approach on a diverse set of text summarization tasks and show that Pegasus finetuned models with entity chains clearly outperform regular finetuning in terms of entity accuracy. We further demonstrate that our simple method can be easily used for pretraining summarization models to do entity-level content planning and summary generation. We see further gains with pretraining.
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Stepwise Extractive Summarization and Planning with Structured Transformers
Jakub Adamek
Blaž Bratanič
Ryan Thomas Mcdonald
Proceedings of The 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Virtual, 4143–4159
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We propose encoder-centric stepwise models for extractive summarization using structured transformers -- HiBERT and Extended Transformers. We enable stepwise summarization by injecting the previously generated summary into the structured transformer as an auxiliary sub-structure. Our models are not only efficient in modeling the structure of long inputs, but they also do not rely on task-specific redundancy-aware modeling, making them a general purpose extractive content planner for different tasks. When evaluated on CNN/DailyMail extractive summarization, stepwise models achieve state-of-the-art performance in terms of Rouge without any redundancy aware modeling or sentence filtering. This also holds true for Rotowire table-to-text generation, where our models surpass previously reported metrics for content selection, planning and ordering, highlighting the strength of stepwise modeling. Amongst the two structured transformers we test, stepwise Extended Transformers provides the best performance across both datasets and sets a new standard for these challenges.
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On Faithfulness and Factuality in Abstractive Summarization
Ryan Thomas Mcdonald
Proceedings of The 58th Annual Meeting of the Association for Computational Linguistics (ACL) (2020)
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It is well known that the standard likelihood training and approximate decoding objectives in neural text generation models are fundamentally flawed and lead to dull and repetitive responses. We found that these models when tested on abstractive summarization are highly prone to hallucinate content that is either unfaithful to the input document, completely irrelevant or gibberish. We conduct a large scale human evaluation of several state of the art neural abstractive summarization systems including pretrained language models to better understand the types of hallucinations. Furthermore, we study the extent to which the hallucinated content (i) co-occurs with the common linguistic irregularities such as repetition and incoherence, and (ii) can be measured by NLU measures such as textual entailment, question answering and OpenIE-based fact checking.
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Leveraging Pre-trained Checkpoints for Sequence Generation Tasks
Transactions of the Association for Computational Linguistics, vol. 8 (2020), pp. 264-280
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Pre-training Neural Networks have become widely successful in Natural Language Processing.
Training these large models on unsupervised data is costly and often not feasible.
We therefore concentrate on publicly available checkpoints.
While most of them improve the Natural Language Understanding, we investigate initializing Transformer-based Sequence-to-sequence models with these pre-trained models for Natural Language Understanding and Generation.
Using these pre-trained models we achieve new state-of-the-art results on Machine translation, Summarization and Sentence Splitting/Fusion.
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