Team SVMrank: Leveraging Feature-rich Support Vector Machines for Ranking Explanations to Elementary Science Questions (D19-53)
Copied to clipboard
| Challenge: | TextGraphs 2019 Shared Task on Multi-Hop Inference for Explanation Regeneration tackles explanation generation for elementary science questions. |
| Approach: | They propose a hybrid pipelined machine learning model and rule-based system to address MIER-19 . they use a featurerich learning-to-rank machine learning and a rule-driven system to rerank the LTR model predictions. |
| Outcome: | The proposed model was ranked fourth in the evaluation, close to the second and third ranked teams, achieving 39.4% MAP. |
Similar Papers
ASU at TextGraphs 2019 Shared Task: Explanation ReGeneration using Language Models and Iterative Re-Ranking (D19-53)
Copied to clipboard
| Challenge: | Explanation Regeneration task is an intermediate step towards general multi-hop inference on large graphs. |
| Approach: | They propose a system that performs multi-hop inference and ranks a set of explanatory facts for a given elementary science question and correct answer pair. |
| Outcome: | The proposed system secured 2nd rank in the text graphs 2019 shared task with a mean average precision (MAP) of 41.3% on the test set. |
TextGraphs 2019 Shared Task on Multi-Hop Inference for Explanation Regeneration (D19-53)
Copied to clipboard
| Challenge: | Detailed extended analyses of all submitted systems showed large relative improvements in accessing the most challenging multi-hop inference problems, while absolute performance remains low. |
| Approach: | The Shared Task on Multi-Hop Inference for Explanation Regeneration asks participants to regenerate detailed gold explanations for elementary science questions by selecting facts from a knowledge base of semi-structured tables. |
| Outcome: | The top-performing system achieved a mean average precision of 0.56 . the task combines facts from a knowledge base and supervised training data . |
Chains-of-Reasoning at TextGraphs 2019 Shared Task: Reasoning over Chains of Facts for Explainable Multi-hop Inference (D19-53)
Copied to clipboard
| Challenge: | EMNLP 2019 shared task on 'Multi-hop Inference Explanation Regeneration' identifies chains of facts relevant to explain an answer to an elementary science examination question. |
| Approach: | They propose a system that identifies chains of facts relevant to explain an answer to an elementary science examination question. |
| Outcome: | The proposed system outperforms the second best system by 14.95 points on the mean average precision (MAP) metric. |
Red Dragon AI at TextGraphs 2019 Shared Task: Language Model Assisted Explanation Generation (D19-53)
Copied to clipboard
| Challenge: | The TextGraphs-13 Shared Task on Explanation Regeneration asked participants to develop methods to reconstruct gold explanations for elementary science questions. |
| Approach: | The TextGraphs-13 Shared Task on Explanation Regeneration asked participants to develop methods to reconstruct gold explanations for elementary science questions. |
| Outcome: | The Explanation Regeneration shared task asked participants to develop methods to reconstruct gold explanations for elementary science questions. |
Autoregressive Reasoning over Chains of Facts with Transformers (2020.coling-main)
Copied to clipboard
| Challenge: | Existing work on this task either evaluates facts in isolation or artificially limits the possible chains of facts, thus limiting multi-hop inference. |
| Approach: | They propose an iterative inference algorithm that decomposes the selection of facts from a corpus autoregressively and conditioning the next iteration on previously selected facts. |
| Outcome: | The proposed method outperforms the previous state-of-the-art in terms of precision, training time and inference efficiency on the WorldTree dataset. |
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. |
Generating Hierarchical Explanations on Text Classification via Feature Interaction Detection (2020.acl-main)
Copied to clipboard
| Challenge: | Existing methods for generating explanations for neural networks ignore feature interactions between words and phrases. |
| Approach: | They propose to build hierarchical explanations by detecting feature interactions by combining words and phrases at different levels of the hierarchy. |
| Outcome: | The proposed method is evaluated on two benchmark datasets, via automatic and human evaluations. |
On the Challenges of Evaluating Compositional Explanations in Multi-Hop Inference: Relevance, Completeness, and Expert Ratings (2021.emnlp-main)
Copied to clipboard
| Challenge: | a large corpus of domain-expert relevance ratings augments a corpus for compositional explanations . a writer's study shows that the evaluations of compositional inference models underestimate performance . |
| Approach: | They construct a corpus of 126k domain-expert relevance ratings that augment explanations to standardized science exam questions. |
| Outcome: | The results show that evaluations underestimate performance of compositional explanations . they show that models regularly discover and produce valid explanations that are different than gold explanations. |
Reward Engineering for Generating Semi-structured Explanation (2024.findings-eacl)
Copied to clipboard
| Challenge: | Unstructured natural language explanations lack a comprehensive explanation mechanism to verify a model's true reasoning capabilities. |
| Approach: | They propose a reward engineering method which uses semi-structured explanations to verify a model's true reasoning capabilities. |
| Outcome: | The proposed method achieves new state-of-the-art on two semi-structured explanation generation benchmarks (ExplaGraph and COPA-SSE) . |
NLProlog: Reasoning with Weak Unification for Question Answering in Natural Language (P19-1)
Copied to clipboard
| Challenge: | ambiguity in natural language is difficult to interpret due to large linguistic variability. |
| Approach: | They propose to use a Prolog prover to extend neural networks with logic programming to solve multi-hop reasoning tasks over natural language. |
| Outcome: | The proposed model outperforms baseline models on two question answering tasks and is competitive on the MedHop corpus. |