Search Results for author:
Found 25 papers, 14 papers with code
Role of Language Relatedness in Multilingual Fine-tuning of Language Models: A Case Study in Indo-Aryan Languages
We hypothesize and validate that multilingual fine-tuning of pre-trained language models can yield better performance on downstream NLP applications, compared to models fine-tuned on individual languages.
Multiple Choice Question Answering (MCQA)
Natural Language Inference
+2
Topic Transferable Table Question Answering
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.
Representation based meta-learning for few-shot spoken intent recognition
Spoken intent detection has become a popular approach to interface with various smart devices with ease.
AIT-QA: Question Answering Dataset over Complex Tables in the Airline Industry
Recent advances in transformers have enabled Table Question Answering (Table QA) systems to achieve high accuracy and SOTA results on open domain datasets like WikiTableQuestions and WikiSQL.
Multilingual and code-switching ASR challenges for low resource Indian languages
For this purpose, we provide a total of ~600 hours of transcribed speech data, comprising train and test sets, in these languages including two code-switched language pairs, Hindi-English and Bengali-English.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Schema Aware Semantic Reasoning for Interpreting Natural Language Queries in Enterprise Settings
We posit that deploying ontology reasoning over domain semantics can help in achieving better natural language understanding for QA systems.
Template Controllable keywords-to-text Generation
The model takes as input a set of un-ordered keywords, and part-of-speech (POS) based template instructions.
Addressing target shift in zero-shot learning using grouped adversarial learning
Zero-shot learning (ZSL) algorithms typically work by exploiting attribute correlations to be able to make predictions in unseen classes.
A Modular Architecture for Unsupervised Sarcasm Generation
In this paper, we propose a novel framework for sarcasm generation; the system takes a literal negative opinion as input and translates it into a sarcastic version.
Storytelling from Structured Data and Knowledge Graphs : An NLG Perspective
In this tutorial, we wish to cover the foundational, methodological, and system development aspects of translating structured data (such as data in tabular form) and knowledge bases (such as knowledge graphs) into natural language.
Unified Semantic Parsing with Weak Supervision
Semantic parsing over multiple knowledge bases enables a parser to exploit structural similarities of programs across the multiple domains.
REVISTING NEGATIVE TRANSFER USING ADVERSARIAL LEARNING
An unintended consequence of feature sharing is the model fitting to correlated tasks within the dataset, termed negative transfer.
Complex Program Induction for Querying Knowledge Bases in the Absence of Gold Programs
On one of the hardest class of programs (comparative reasoning) with 5{--}10 steps, CIPITR outperforms NSM by a factor of 89 and memory networks by 9 times.
On Controllable Sparse Alternatives to Softmax
Converting an n-dimensional vector to a probability distribution over n objects is a commonly used component in many machine learning tasks like multiclass classification, multilabel classification, attention mechanisms etc.
Unsupervised Neural Text Simplification
The paper presents a first attempt towards unsupervised neural text simplification that relies only on unlabeled text corpora.
Ranked #2 on
Text Simplification
on ASSET
Scalable Micro-planned Generation of Discourse from Structured Data
We present a framework for generating natural language description from structured data such as tables; the problem comes under the category of data-to-text natural language generation (NLG).
Unsupervised Controllable Text Formalization
We propose a novel framework for controllable natural language transformation.
Modeling Topical Coherence in Discourse without Supervision
Our evaluation shows that the measured coherence scores are positively correlated with the ground truth for both the datasets.
DuoRC: Towards Complex Language Understanding with Paraphrased Reading Comprehension
We propose DuoRC, a novel dataset for Reading Comprehension (RC) that motivates several new challenges for neural approaches in language understanding beyond those offered by existing RC datasets.
Generating Descriptions from Structured Data Using a Bifocal Attention Mechanism and Gated Orthogonalization
For example, while generating descriptions from a table, a human would attend to information at two levels: (i) the fields (macro level) and (ii) the values within the field (micro level).
A Mixed Hierarchical Attention based Encoder-Decoder Approach for Standard Table Summarization
Structured data summarization involves generation of natural language summaries from structured input data.
Complex Sequential Question Answering: Towards Learning to Converse Over Linked Question Answer Pairs with a Knowledge Graph
Further, unlike existing large scale QA datasets which contain simple questions that can be answered from a single tuple, the questions in our dialogs require a larger subgraph of the KG.
A Machine Learning Approach for Evaluating Creative Artifacts
Our proposed learning framework is applicable to all creative domains; yet we evaluate it on a dataset of movies created from IMDb and Rotten Tomatoes due to availability of audience and critic scores, which can be used as proxy ground truth labels for creativity.
Story Generation from Sequence of Independent Short Descriptions
Existing Natural Language Generation (NLG) systems are weak AI systems and exhibit limited capabilities when language generation tasks demand higher levels of creativity, originality and brevity.
Towards Building Large Scale Multimodal Domain-Aware Conversation Systems
With this dataset, we propose 5 new sub-tasks for multimodal conversations along with their evaluation methodology.
