Papers by Karthik Sankaranarayanan
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. |
Role of Language Relatedness in Multilingual Fine-tuning of Language Models: A Case Study in Indo-Aryan Languages (2021.emnlp-main)
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| Challenge: | A study of multilingual fine-tuning yields better performance on downstream NLP applications . low resource languages such as Oriya and Punjabi are found to be the largest beneficiaries of multi-lingual fine tuning. |
| Approach: | They propose to leverage the relatedness of languages that belong to the same family in NLP models by multilingual fine-tuning. |
| Outcome: | The proposed approach improves performance on downstream NLP tasks by 15% compared to monolingual fine-tuning. |
AIT-QA: Question Answering Dataset over Complex Tables in the Airline Industry (2022.naacl-industry)
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Yannis Katsis, Saneem Chemmengath, Vishwajeet Kumar, Samarth Bharadwaj, Mustafa Canim, Michael Glass, Alfio Gliozzo, Feifei Pan, Jaydeep Sen, Karthik Sankaranarayanan, Soumen Chakrabarti
| Challenge: | Table Question Answering (Table QA) systems have been shown to be highly accurate when trained and tested on open-domain datasets built on top of Wikipedia tables. |
| Approach: | They propose a domain-specific Table QA test dataset to test Table Question Answering systems on open-domain datasets built on top of Wikipedia tables. |
| Outcome: | The proposed methods are highly accurate when tested on open-domain datasets built on top of Wikipedia tables. |
Topic Transferable Table Question Answering (2021.emnlp-main)
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Saneem Chemmengath, Vishwajeet Kumar, Samarth Bharadwaj, Jaydeep Sen, Mustafa Canim, Soumen Chakrabarti, Alfio Gliozzo, Karthik Sankaranarayanan
| Challenge: | Weakly-supervised table question-answering (TableQA) models have achieved state-of-art performance by using pre-trained BERT transformer to jointly encoding a question and a table to produce structured query for the question. |
| Approach: | They propose a framework for TableQA that incorporates topic-specific vocabulary injection into BERT, a novel text-to-text transformer generator and a logical form re-ranker. |
| Outcome: | The proposed framework provides a reasonably good baseline for topic shift benchmarks. |
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. |
A Modular Architecture for Unsupervised Sarcasm Generation (D19-1)
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| Challenge: | Existing systems for sarcasm generation are elusive due to the fact that both selection of contents and training of sarcasm are based on the same data. |
| Approach: | They propose a framework that takes a literal negative opinion as input and translates it into a sarcastic version. |
| Outcome: | The proposed system outperforms baselines built using known unsupervised statistical and neural machine translation and style transfer techniques. |
Schema Aware Semantic Reasoning for Interpreting Natural Language Queries in Enterprise Settings (2020.coling-main)
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| Challenge: | Using ontology reasoning to understand natural language is a challenge for QA systems . a recent study shows that ontologies can improve natural language understanding . |
| Approach: | They propose to use ontology reasoning to translate natural language interpretation into a sequence of solvable tasks by an ontologist. |
| Outcome: | The proposed framework achieves better natural language understanding with a 30% accuracy improvement over the current state of natural language query interfaces. |
DuoRC: Towards Complex Language Understanding with Paraphrased Reading Comprehension (P18-1)
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| Challenge: | DuoRC contains 186,089 unique question-answer pairs created from 7680 movie plots . |
| Approach: | They propose a novel dataset for Reading Comprehension that motivates new challenges for neural approaches in language understanding beyond those offered by existing RC datasets. |
| Outcome: | The proposed dataset motivates several new challenges for neural approaches in language understanding beyond those offered by existing RC datasets. |
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. |