Search Results for author:
Found 17 papers, 5 papers with code
CABINET: Content Relevance based Noise Reduction for Table Question Answering
Typically, only a small part of the whole table is relevant to derive the answer for a given question.
Ranked #1 on
Semantic Parsing
on WikiSQL
(Denotation accuracy (test) metric)
Dialogue Agents 101: A Beginner's Guide to Critical Ingredients for Designing Effective Conversational Systems
To this end, we propose UNIT, a UNified dIalogue dataseT constructed from conversations of existing datasets for different dialogue tasks capturing the nuances for each of them.
INGENIOUS: Using Informative Data Subsets for Efficient Pre-Training of Language Models
A salient characteristic of pre-trained language models (PTLMs) is a remarkable improvement in their generalization capability and emergence of new capabilities with increasing model capacity and pre-training dataset size.
One-Shot Doc Snippet Detection: Powering Search in Document Beyond Text
MONOMER fuses context from visual, textual, and spatial modalities of snippets and documents to find query snippet in target documents.
Persuasion Strategies in Advertisements
Motivated by persuasion literature in social psychology and marketing, we introduce an extensive vocabulary of persuasion strategies and build the first ad image corpus annotated with persuasion strategies.
LM-CORE: Language Models with Contextually Relevant External Knowledge
Large transformer-based pre-trained language models have achieved impressive performance on a variety of knowledge-intensive tasks and can capture factual knowledge in their parameters.
INDIGO: Intrinsic Multimodality for Domain Generalization
To this end, we propose IntriNsic multimodality for DomaIn GeneralizatiOn (INDIGO), a simple and elegant way of leveraging the intrinsic modality present in these pre-trained multimodal networks along with the visual modality to enhance generalization to unseen domains at test-time.
CoSe-Co: Text Conditioned Generative CommonSense Contextualizer
To train CoSe-Co, we propose a novel dataset comprising of sentence and commonsense knowledge pairs.
No Need to Know Everything! Efficiently Augmenting Language Models With External Knowledge
This allows the training of the language model to be de-coupled from the external knowledge source and the latter can be updated without affecting the parameters of the language model.
CoSe-Co: Sentence Conditioned Generative CommonSense Contextualizer for Language Models
Pre-trained Language Models (PTLMs) have been shown to perform well on natural language reasoning tasks requiring commonsense.
Multi-Modal Association based Grouping for Form Structure Extraction
Experimental results show the effectiveness of our approach achieving a recall of 90. 29%, 73. 80%, 83. 12%, and 52. 72% for the above structures, respectively, outperforming semantic segmentation baselines significantly.
Form2Seq : A Framework for Higher-Order Form Structure Extraction
To mitigate this, we propose Form2Seq, a novel sequence-to-sequence (Seq2Seq) inspired framework for structure extraction using text, with a specific focus on forms, which leverages relative spatial arrangement of structures.
TAN-NTM: Topic Attention Networks for Neural Topic Modeling
Topic models have been widely used to learn text representations and gain insight into document corpora.
SHERLock: Self-Supervised Hierarchical Event Representation Learning
Also, they are believed to be arranged hierarchically, allowing for an efficient representation of complex long-horizon experiences.
Document Structure Extraction using Prior based High Resolution Hierarchical Semantic Segmentation
We introduce our new human-annotated forms dataset and show that our method significantly outperforms different segmentation baselines on this dataset in extracting hierarchical structures.
ReDecode Framework for Iterative Improvement in Paraphrase Generation
Generating paraphrases, that is, different variations of a sentence conveying the same meaning, is an important yet challenging task in NLP.
Improving Search through A3C Reinforcement Learning based Conversational Agent
We develop a reinforcement learning based search assistant which can assist users through a set of actions and sequence of interactions to enable them realize their intent.
