Papers by Mokanarangan Thayaparan
Unification-based Reconstruction of Multi-hop Explanations for Science Questions (2021.eacl-main)
Copied to clipboard
| Challenge: | Existing approaches build explanations considering each question in isolation, but new approach leverages explanatory patterns emerging in scientific explanations. |
| Approach: | They propose a framework for reconstructing multi-hop explanations in science Question Answering . they integrate lexical relevance with the notion of unification power to rank atomic facts . |
| Outcome: | The proposed method achieves results competitive with Transformers, but is faster and scalable to large explanatory corpora. |
Does My Representation Capture X? Probe-Ably (2021.acl-demo)
Copied to clipboard
| Challenge: | Probing (or diagnostic classification) has become a popular strategy for investigating whether a given set of intermediate features is present in the representations of neural models. |
| Approach: | They propose to use an extendable probing framework to automate the application of probing methods to the user’s inputs. |
| Outcome: | The proposed framework automates the application of probing methods to the user’s inputs. |
Graph Based Semi-Supervised Learning Approach for Tamil POS tagging (L18-1)
Copied to clipboard
| Challenge: | Parts of Speech (POS) tagging is challenging for low resourced languages such as Tamil . low resource Tamil does not have large POS annotated corpus to build good quality POS taggers using supervised machine learning techniques. |
| Approach: | They propose a graph-based semi-supervised learning approach to classify unlabelled data using a small POS labelled data set. |
| Outcome: | The proposed method achieves 0.8743 over 0.7333 produced by a CRF tagger for the same limited size corpus. |
Diff-Explainer: Differentiable Convex Optimization for Explainable Multi-hop Inference (2022.tacl-1)
Copied to clipboard
| Challenge: | Existing explainable multi-hop inference models are regarded as black-boxes due to their ability to transfer linguistic and semantic information to downstream tasks, posing concerns about interpretability and transparency of their predictions. |
| Approach: | They propose a hybrid framework that integrates explicit constraints with neural architectures through differentiable convex optimization to answer and explain multi-hop questions in natural language. |
| Outcome: | The proposed framework improves performance on scientific and commonsense QA tasks while still providing structured explanations in support of its predictions. |
A Differentiable Integer Linear Programming Solver for Explanation-Based Natural Language Inference (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing ILP frameworks are non-differentiable and cannot be integrated as part of a broader deep learning architecture. |
| Approach: | They propose a neuro-symbolic architecture for explanation-based NLI based on DBCS. |
| Outcome: | The proposed approach achieves superior performance when compared to existing solvers and black-box solver. |
Explainable Inference Over Grounding-Abstract Chains for Science Questions (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing inference models for science questions are black-box by nature, lacking explanations for their predictions. |
| Approach: | They propose an explainable inference approach for science questions by reasoning on grounding and abstract inference chains. |
| Outcome: | The proposed model generates plausible explanations for science questions using a weighted graph of relevant facts and a Bayesian Optimisation formalism. |
To be or not to be an Integer? Encoding Variables for Mathematical Text (2022.findings-acl)
Copied to clipboard
| Challenge: | a number of natural language inference models are limited in interpreting mathematical knowledge written in Natural Language . a variable's meaning is determined exclusively by its defining type, i.e., its context . |
| Approach: | They propose a method that can create context-based representations for variables . they propose 'variable slot' approach which can be used to model variables based on their meaning . |
| Outcome: | The proposed model can be used to represent variables in natural language . it can be applied to a task of variable typing and create context-based representations for variables . |
Identifying Supporting Facts for Multi-hop Question Answering with Document Graph Networks (D19-53)
Copied to clipboard
| Challenge: | Recent advances in reading comprehension have resulted in models that surpass human performance when the answer is contained in a single, continuous passage of text. |
| Approach: | They propose a document-structured message passing architecture for the identification of supporting facts over a graph-structure based representation of text. |
| Outcome: | The proposed model outperforms a baseline reading comprehension test on raw text and shows that it is relevant for multi-hop reasoning. |
Case-Based Abductive Natural Language Inference (2022.coling-1)
Copied to clipboard
| Challenge: | Recent approaches for multi-hop inference construct explanations considering each test case in isolation, but semantic drift causes wrong conclusions. |
| Approach: | They propose an abductive framework for multi-hop NLI exploring the retrieve-reuse-refine paradigm in Case-Based Reasoning. |
| Outcome: | The proposed model can be integrated with sparse and dense pre-trained encoders to improve multi-hop inference, or adopted as an evidence retriever for Transformers. |