2023
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LOWRECORP: the Low-Resource NLG Corpus Building Challenge
Khyathi Raghavi Chandu
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David M. Howcroft
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Dimitra Gkatzia
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Yi-Ling Chung
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Yufang Hou
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Chris Chinenye Emezue
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Pawan Rajpoot
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Tosin Adewumi
Proceedings of the 16th International Natural Language Generation Conference: Generation Challenges
Most languages in the world do not have sufficient data available to develop neural-network-based natural language generation (NLG) systems. To alleviate this resource scarcity, we propose a novel challenge for the NLG community: low-resource language corpus development (LOWRECORP). We present an innovative framework to collect a single dataset with dual tasks to maximize the efficiency of data collection efforts and respect language consultant time. Specifically, we focus on a text-chat-based interface for two generation tasks – conversational response generation grounded in a source document and/or image and dialogue summarization (from the former task). The goal of this shared task is to collectively develop grounded datasets for local and low-resourced languages. To enable data collection, we make available web-based software that can be used to collect these grounded conversations and summaries. Submissions will be assessed for the size, complexity, and diversity of the corpora to ensure quality control of the datasets as well as any enhancements to the interface or novel approaches to grounding conversations.
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Nearest Neighbor Search over Vectorized Lexico-Syntactic Patterns for Relation Extraction from Financial Documents
Pawan Rajpoot
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Ankur Parikh
Proceedings of the 2nd Workshop on Pattern-based Approaches to NLP in the Age of Deep Learning
Relation extraction (RE) has achieved remarkable progress with the help of pre-trained language models. However, existing RE models are usually incapable of handling two situations: implicit expressions and long-tail relation classes, caused by language complexity and data sparsity. Further, these approaches and models are largely inaccessible to users who don’t have direct access to large language models (LLMs) and/or infrastructure for supervised training or fine-tuning. Rule-based systems also struggle with implicit expressions. Apart from this, Real world financial documents such as various 10-X reports (including 10-K, 10-Q, etc.) of publicly traded companies pose another challenge to rule-based systems in terms of longer and complex sentences. In this paper, we introduce a simple approach that consults training relations at test time through a nearest-neighbor search over dense vectors of lexico-syntactic patterns and provides a simple yet effective means to tackle the above issues. We evaluate our approach on REFinD and show that our method achieves state-of-the-art performance. We further show that it can provide a good start for human in the loop setup when a small number of annotations are available and it is also beneficial when domain experts can provide high quality patterns. Our code is available at 1.
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teamPN at SemEval-2023 Task 1: Visual Word Sense Disambiguation Using Zero-Shot MultiModal Approach
Nikita Katyal
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Pawan Rajpoot
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Subhanandh Tamilarasu
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Joy Mustafi
Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
Visual Word Sense Disambiguation shared task at SemEval-2023 aims to identify an image corresponding to the intended meaning of a given ambiguous word (with related context) from a set of candidate images. The lack of textual description for the candidate image and the corresponding word’s ambiguity makes it a challenging problem. This paper describes teamPN’s multi-modal and modular approach to solving this in English track of the task. We efficiently used recent multi-modal pre-trained models backed by real-time multi-modal knowledge graphs to augment textual knowledge for the images and select the best matching image accordingly. We outperformed the baseline model by ~5 points and proposed a unique approach that can further work as a framework for other modular and knowledge-backed solutions.
2022
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teamPN at TSAR-2022 Shared Task: Lexical Simplification using Multi-Level and Modular Approach
Nikita Nikita
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Pawan Rajpoot
Proceedings of the Workshop on Text Simplification, Accessibility, and Readability (TSAR-2022)
Lexical Simplification is the process of reducing the lexical complexity of a text by replacing difficult words with easier-to-read (or understand) expressions while preserving the original information and meaning. This paper explains the work done by our team “teamPN” for the English track of TSAR 2022 Shared Task of Lexical Simplification. We created a modular pipeline which combines transformers based models with traditional NLP methods like paraphrasing and verb sense disambiguation. We created a multi-level and modular pipeline where the target text is treated according to its semantics (Part of Speech Tag). The pipeline is multi-level as we utilize multiple source models to find potential candidates for replacement. It is modular as we can switch the source models and their weighting in the final re-ranking.