1 code implementation • ACL 2022 • Keshav Kolluru, Muqeeth Mohammed, Shubham Mittal, Soumen Chakrabarti, Mausam .
Progress with supervised Open Information Extraction (OpenIE) has been primarily limited to English due to the scarcity of training data in other languages.
Ranked #1 on
Open Information Extraction
on OpenIE
no code implementations • CoNLL (EMNLP) 2021 • Arijit Nag, Bidisha Samanta, Animesh Mukherjee, Niloy Ganguly, Soumen Chakrabarti
Data collection is challenging for Indian languages, because they are syntactically and morphologically diverse, as well as different from resource-rich languages like English.
no code implementations • 10 Jan 2023 • Abhijeet Awasthi, Soumen Chakrabarti, Sunita Sarawagi
To the best of our knowledge, we are the first to attempt inference-time adaptation of Text-to-SQL models, and harness trainable structured similarity between subqueries.
no code implementations • 13 Nov 2022 • Shubham Mittal, Keshav Kolluru, Soumen Chakrabarti, Mausam
Automated completion of open knowledge bases (KBs), which are constructed from triples of the form (subject phrase, relation phrase, object phrase) obtained via open information extraction (IE) from text, is useful for discovering novel facts that may not directly be present in the text.
no code implementations • 20 Oct 2022 • Indradyumna Roy, Soumen Chakrabarti, Abir De
A common consideration for scoring similarity is the maximum common subgraph (MCS) between the query and corpus graphs, usually counting the number of common edges (i. e., MCES).
no code implementations • 20 Oct 2022 • Abir De, Soumen Chakrabarti
We do not draw the concave function from a restricted family, but rather learn from data using a highly expressive neural network that implements a differentiable quadrature procedure.
no code implementations • 12 Oct 2022 • Aditya Sharma, Apoorv Saxena, Chitrank Gupta, Seyed Mehran Kazemi, Partha Talukdar, Soumen Chakrabarti
Recent years have witnessed much interest in temporal reasoning over knowledge graphs (KG) for complex question answering (QA), but there remains a substantial gap in human capabilities.
2 code implementations • 3 Jan 2022 • Subhabrata Dutta, Samiya Caur, Soumen Chakrabarti, Tanmoy Chakraborty
Detecting and labeling stance in social media text is strongly motivated by hate speech detection, poll prediction, engagement forecasting, and concerted propaganda detection.
no code implementations • 14 Dec 2021 • Vishwajeet Kumar, Saneem Chemmengath, Yash Gupta, Jaydeep Sen, Samarth Bharadwaj, Soumen Chakrabarti
This leads to a noisy multi-instance training regime involving not only rows of the table, but also spans of linked text.
Ranked #1 on
Question Answering
on HybridQA
no code implementations • 18 Oct 2021 • Arijit Nag, Bidisha Samanta, Animesh Mukherjee, Niloy Ganguly, Soumen Chakrabarti
Relation classification (sometimes called 'extraction') requires trustworthy datasets for fine-tuning large language models, as well as for evaluation.
1 code implementation • NeurIPS 2021 • Subhabrata Dutta, Tanya Gautam, Soumen Chakrabarti, Tanmoy Chakraborty
The Transformer and its variants have been proven to be efficient sequence learners in many different domains.
1 code implementation • EMNLP 2021 • Saneem Ahmed Chemmengath, Vishwajeet Kumar, Samarth Bharadwaj, Jaydeep Sen, Mustafa Canim, Soumen Chakrabarti, Alfio Gliozzo, Karthik Sankaranarayanan
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.
no code implementations • 23 Aug 2021 • Chitrank Gupta, Yash Jain, Abir De, Soumen Chakrabarti
In recent years, inductive graph embedding models, \emph{viz.
1 code implementation • NAACL (ACL) 2022 • Yannis Katsis, Saneem Chemmengath, Vishwajeet Kumar, Samarth Bharadwaj, Mustafa Canim, Michael Glass, Alfio Gliozzo, Feifei Pan, Jaydeep Sen, Karthik Sankaranarayanan, Soumen Chakrabarti
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.
1 code implementation • 13 Jun 2021 • Subhabrata Dutta, Shravika Mittal, Dipankar Das, Soumen Chakrabarti, Tanmoy Chakraborty
Second, there is a measurable positive correlation between the novelty of the root content (with respect to a streaming external corpus) and the relative size of the resulting cascade.
2 code implementations • ACL 2021 • Apoorv Saxena, Soumen Chakrabarti, Partha Talukdar
Temporal Knowledge Graphs (Temporal KGs) extend regular Knowledge Graphs by providing temporal scopes (start and end times) on each edge in the KG.
Ranked #5 on
Question Answering
on CronQuestions
no code implementations • AKBC 2021 • Harkanwar Singh, Prachi Jain, Mausam, Soumen Chakrabarti
Almost all of existing KGC research is applicable to only one KG at a time, and in one language only.
Ranked #2 on
Knowledge Graph Completion
on DBP-5L (Greek)
no code implementations • 9 Mar 2021 • Aman Jain, Mayank Kothyari, Vishwajeet Kumar, Preethi Jyothi, Ganesh Ramakrishnan, Soumen Chakrabarti
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.
no code implementations • 21 Jan 2021 • Rima Hazra, Hardik Aggarwal, Pawan Goyal, Animesh Mukherjee, Soumen Chakrabarti
This "social network of code" is rarely studied by social network researchers.
1 code implementation • 13 Dec 2020 • Indradyumna Roy, Abir De, Soumen Chakrabarti
Sequence encoders are more expressive, but are permutation sensitive by design.
1 code implementation • EMNLP 2020 • Keshav Kolluru, Vaibhav Adlakha, Samarth Aggarwal, Mausam, Soumen Chakrabarti
This IGL based coordination analyzer helps our OpenIE system handle complicated coordination structures, while also establishing a new state of the art on the task of coordination analysis, with a 12. 3 pts improvement in F1 over previous analyzers.
Ranked #1 on
Open Information Extraction
on WiRe57
no code implementations • Findings of the Association for Computational Linguistics 2020 • Sahil Shah, Vihari Piratla, Soumen Chakrabarti, Sunita Sarawagi
Each client uses an unsupervised, corpus-based sketch to register to the service.
1 code implementation • ACL 2020 • Keshav Kolluru, Samarth Aggarwal, Vipul Rathore, Mausam, Soumen Chakrabarti
While traditional systems for Open Information Extraction were statistical and rule-based, recently neural models have been introduced for the task.
1 code implementation • 2 May 2020 • Prachi Jain, Sushant Rathi, Mausam, Soumen Chakrabarti
Most existing methods train with a small number of negative samples for each positive instance in these datasets to save computational costs.
1 code implementation • EMNLP 2020 • Prachi Jain, Sushant Rathi, Mausam, Soumen Chakrabarti
Temporal knowledge bases associate relational (s, r, o) triples with a set of times (or a single time instant) when the relation is valid.
Ranked #1 on
Link Prediction
on Wikidata12k
no code implementations • 3 Nov 2019 • Sahana Ramnath, Amrita Saha, Soumen Chakrabarti, Mitesh M. Khapra
With the prolification of multimodal interaction in various domains, recently there has been much interest in text based image retrieval in the computer vision community.
no code implementations • 20 Jul 2019 • Abir De, Soumen Chakrabarti
Link prediction (LP) algorithms propose to each node a ranked list of nodes that are currently non-neighbors, as the most likely candidates for future linkage.
1 code implementation • 21 Jun 2019 • Bidisha Samanta, Sharmila Reddy, Hussain Jagirdar, Niloy Ganguly, Soumen Chakrabarti
Code-switching, the interleaving of two or more languages within a sentence or discourse is pervasive in multilingual societies.
1 code implementation • ACL 2019 • Bidisha Samanta, Niloy Ganguly, Soumen Chakrabarti
Consequently, the best monolingual methods perform relatively poorly on code-switched text.
1 code implementation • ACL 2019 • Vihari Piratla, Sunita Sarawagi, Soumen Chakrabarti
Given a small corpus $\mathcal D_T$ pertaining to a limited set of focused topics, our goal is to train embeddings that accurately capture the sense of words in the topic in spite of the limited size of $\mathcal D_T$.
no code implementations • TACL 2019 • Amrita Saha, Ghulam Ahmed Ansari, Abhishek Laddha, Karthik Sankaranarayanan, Soumen Chakrabarti
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.
1 code implementation • 8 Feb 2019 • Divam Gupta, Kushagra Singh, Soumen Chakrabarti, Tanmoy Chakraborty
The auxiliary and main GRUs send their states to a different fully connected layer, trained for the main task.
1 code implementation • 28 Nov 2018 • Divam Gupta, Tanmoy Chakraborty, Soumen Chakrabarti
A primary instance is also submitted to each auxiliary RNN, but their state sequences are gated and merged into a novel composite state sequence tailored to the primary inference task.
1 code implementation • ACL 2018 • Prachi Jain, Pankaj Kumar, {Mausam}, Soumen Chakrabarti
State-of-the-art knowledge base completion (KBC) models predict a score for every known or unknown fact via a latent factorization over entity and relation embeddings.
1 code implementation • ICLR 2018 • Shiv Shankar, Vihari Piratla, Soumen Chakrabarti, Siddhartha Chaudhuri, Preethi Jyothi, Sunita Sarawagi
We present CROSSGRAD, a method to use multi-domain training data to learn a classifier that generalizes to new domains.
Ranked #69 on
Domain Generalization
on PACS
no code implementations • 13 Feb 2018 • Mayank Singh, Rajdeep Sarkar, Atharva Vyas, Pawan Goyal, Animesh Mukherjee, Soumen Chakrabarti
We propose several approaches to rank papers from these noisy 'match' outcomes.
2 code implementations • 2 Jun 2017 • Prachi Jain, Shikhar Murty, Mausam, Soumen Chakrabarti
If not, what characteristics of a dataset determine the performance of MF and TF models?
no code implementations • 17 Oct 2013 • Abir De, Niloy Ganguly, Soumen Chakrabarti
Apart from the new predictor, another contribution is a rigorous protocol for benchmarking and reporting LP algorithms, which reveals the regions of strengths and weaknesses of all the predictors studied here, and establishes the new proposal as the most robust.