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Found 24 papers, 10 papers with code
WARM: A Weakly (+Semi) Supervised Math Word Problem Solver
In order to address this challenge of equation annotation, we propose a weakly supervised model for solving MWPs by requiring only the final answer as supervision.
MILU: A Multi-task Indic Language Understanding Benchmark
Models also perform better in high resource languages as compared to low resource ones.
Benchmarking and Building Zero-Shot Hindi Retrieval Model with Hindi-BEIR and NLLB-E5
Given the large number of Hindi speakers worldwide, there is a pressing need for robust and efficient information retrieval systems for Hindi.
Mistral-SPLADE: LLMs for better Learned Sparse Retrieval
We use Mistral as the backbone to develop our Learned Sparse Retriever similar to SPLADE and train it on a subset of sentence-transformer data which is often used for training text embedding models.
Hindi-BEIR : A Large Scale Retrieval Benchmark in Hindi
Given the large number of Hindi speakers worldwide, there is a pressing need for robust and efficient information retrieval systems for Hindi.
INDIC QA BENCHMARK: A Multilingual Benchmark to Evaluate Question Answering capability of LLMs for Indic Languages
Large Language Models (LLMs) have demonstrated remarkable zero-shot and few-shot capabilities in unseen tasks, including context-grounded question answering (QA) in English.
PrimeQA: The Prime Repository for State-of-the-Art Multilingual Question Answering Research and Development
The field of Question Answering (QA) has made remarkable progress in recent years, thanks to the advent of large pre-trained language models, newer realistic benchmark datasets with leaderboards, and novel algorithms for key components such as retrievers and readers.
Multi-Row, Multi-Span Distant Supervision For Table+Text Question
Question answering (QA) over tables and linked text, also called TextTableQA, has witnessed significant research in recent years, as tables are often found embedded in documents along with related text.
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.
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.
Capturing Row and Column Semantics in Transformer Based Question Answering over Tables
While this model yields extremely high accuracy at finding cell values on recent benchmarks, a second model we propose, called RCI representation, provides a significant efficiency advantage for online QA systems over tables by materializing embeddings for existing tables.
WARM: A Weakly (+Semi) Supervised Model for Solving Math word Problems
We approach this problem by first learning to generate the equation using the problem description and the final answer, which we subsequently use to train a supervised MWP solver.
Select, Substitute, Search: A New Benchmark for Knowledge-Augmented Visual Question Answering
In response, we identify a key structural idiom in OKVQA , viz., S3 (select, substitute and search), and build a new data set and challenge around it.
Meta-Learning for Effective Multi-task and Multilingual Modelling
We present experiments on five different tasks and six different languages from the XTREME multilingual benchmark dataset.
Vocabulary Matters: A Simple yet Effective Approach to Paragraph-level Question Generation
Question generation (QG) has recently attracted considerable attention.
Question Generation from Paragraphs: A Tale of Two Hierarchical Models
Specifically, we propose (a) a novel hierarchical BiLSTM model with selective attention and (b) a novel hierarchical Transformer architecture, both of which learn hierarchical representations of paragraphs.
Putting the Horse before the Cart: A Generator-Evaluator Framework for Question Generation from Text
The \textit{generator} is a sequence-to-sequence model that incorporates the \textit{structure} and \textit{semantics} of the question being generated.
ParaQG: A System for Generating Questions and Answers from Paragraphs
Generating syntactically and semantically valid and relevant questions from paragraphs is useful with many applications.
Cross-Lingual Training for Automatic Question Generation
For a new language, such training instances are hard to obtain making the QG problem even more challenging.
Putting the Horse Before the Cart:A Generator-Evaluator Framework for Question Generation from Text
The {\it generator} is a sequence-to-sequence model that incorporates the {\it structure} and {\it semantics} of the question being generated.
Entity Resolution and Location Disambiguation in the Ancient Hindu Temples Domain using Web Data
We present a system for resolving entities and disambiguating locations based on publicly available web data in the domain of ancient Hindu Temples.
Automating Reading Comprehension by Generating Question and Answer Pairs
Neural network-based methods represent the state-of-the-art in question generation from text.
Civique: Using Social Media to Detect Urban Emergencies
We present the Civique system for emergency detection in urban areas by monitoring micro blogs like Tweets.
