Papers by Parag Jain
A Mixed Hierarchical Attention Based Encoder-Decoder Approach for Standard Table Summarization (N18-2)
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| Challenge: | Structured data summarization involves generation of summaries from structured input data. |
| Approach: | They propose a hierarchical attention-based encoder-decoder model which leverages the structure in addition to the content of the tables. |
| Outcome: | The proposed model improves on the weathergov dataset by 30% over the current state-of-the-art. |
Multi-Document Summarization with Centroid-Based Pretraining (2023.acl-short)
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| Challenge: | In Multi-Document Summarization, the input is a set of documents, and the output is its summary. |
| Approach: | They propose a novel pretraining objective that uses the ROUGE-based centroid of each document cluster as a proxy for its summary. |
| Outcome: | The proposed model is better or comparable to state-of-the-art models. |
STRUCTSUM Generation for Faster Text Comprehension (2024.acl-long)
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| Challenge: | Current large language models (LLMs) fail to adequately structure and organize information in a way that facilitates comprehension. |
| Approach: | They propose a taxonomy of problems around factuality, global and local structure common to both modalities and propose 'auto-QA' to improve the accuracy of generated structured representations. |
| Outcome: | The proposed models improve accuracy and speed without loss of accuracy. |
Unified Semantic Parsing with Weak Supervision (P19-1)
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Priyanka Agrawal, Ayushi Dalmia, Parag Jain, Abhishek Bansal, Ashish Mittal, Karthik Sankaranarayanan
| Challenge: | Semantic parsing over multiple knowledge bases requires high-quality annotations of (utterance, program) pairs. |
| Approach: | They propose a framework to build a unified multi-domain enabled semantic parser with weak supervision. |
| Outcome: | The proposed model improves performance by 20% on the Overnight dataset. |
Storytelling from Structured Data and Knowledge Graphs : An NLG Perspective (P19-4)
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| Challenge: | tutorial aims to explain the basic concepts of translating structured data into natural language . Various solutions for structured data translation will be discussed . |
| Approach: | tutorial aims to cover foundational, methodological, and system development aspects of translating structured data into natural language . Various solutions starting from traditional rule based/heuristic driven and modern data-driven will be discussed . |
| Outcome: | The tutorial aims to convey challenges and nuances in structured data translation, data representation techniques, and domain adaptable solutions for translation of the data into natural language form. |
Generating Descriptions from Structured Data Using a Bifocal Attention Mechanism and Gated Orthogonalization (N18-1)
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| Challenge: | a proposed model for generating natural language descriptions is too generic and does not exploit task specific characteristics. |
| Approach: | They propose a model which uses a fused bifocal attention mechanism to exploit micro and macro level information and a gated orthogonalization mechanism to ensure that a field is remembered for a few time steps and then forgotten. |
| Outcome: | The proposed model improves on a recently released dataset with two similar datasets for French and German. |
Semantic Parsing for Conversational Question Answering over Knowledge Graphs (2023.eacl-main)
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| Challenge: | Recent years have seen an increasing number of applications aiming to build conversational interfaces based on information retrieval and user recommendation. |
| Approach: | They develop a dataset where user questions are annotated with Sparql parses and system answers correspond to execution results thereof. |
| Outcome: | The proposed parsers can be used to ground questions into queries over definitions in a knowledge graph with large vocabularies. |
Conversational Semantic Parsing using Dynamic Context Graphs (2023.emnlp-main)
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| Challenge: | Existing work on conversational semantic parsing has focused on answering questions in isolation . whereas existing work on KBQA is focused on resolving questions in the context of natural language questions . |
| Approach: | They propose to model conversational semantic parsing over general purpose knowledge graphs with millions of entities and thousands of relation-types by exploiting its underlying structure and encoding it with a graph neural network. |
| Outcome: | The proposed model is better at processing discourse information and longer interactions . it is better than static models at handling ellipsis and coreference, the authors show . |
Memory-Based Semantic Parsing (2021.tacl-1)
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| Challenge: | Existing models for context-dependent semantic parsing focus on parse utterances in isolation . a decoder cannot copy or modify the parser from the previous utterrance . |
| Approach: | They propose to represent contextual information using an external memory by maintaining the cumulative meaning of sequential user utterances. |
| Outcome: | The proposed model can better process context-dependent information without task-specific decoders. |
Unsupervised Neural Text Simplification (P19-1)
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| Challenge: | Existing unsupervised methods for text simplification are limited to unlabeled text . paper aims to improve the performance of unsupervised systems by incorporating labeled pairs . |
| Approach: | They propose to use unlabeled text to train a neural text simplification framework . they propose to add a pair of attentional-decoders to the framework to improve performance . |
| Outcome: | The proposed model outperforms existing supervised methods on public test data. |