IITK-RSA at SemEval-2020 Task 5: Detecting Counterfactuals

• URL: http://arxiv.org/abs/2007.10866v1
• Date: Tue, 21 Jul 2020 14:45:53 GMT
• Title: IITK-RSA at SemEval-2020 Task 5: Detecting Counterfactuals
• Authors: Anirudh Anil Ojha, Rohin Garg, Shashank Gupta and Ashutosh Modi
• Abstract summary: This paper describes our efforts in tackling Task 5 of SemEval-2020. The task involved detecting a class of textual expressions known as counterfactuals. Counterfactual statements describe events that have not or could not have occurred and the possible implications of such events.
• Score: 3.0396370700420063
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper describes our efforts in tackling Task 5 of SemEval-2020. The task involved detecting a class of textual expressions known as counterfactuals and separating them into their constituent elements. Counterfactual statements describe events that have not or could not have occurred and the possible implications of such events. While counterfactual reasoning is natural for humans, understanding these expressions is difficult for artificial agents due to a variety of linguistic subtleties. Our final submitted approaches were an ensemble of various fine-tuned transformer-based and CNN-based models for the first subtask and a transformer model with dependency tree information for the second subtask. We ranked 4-th and 9-th in the overall leaderboard. We also explored various other approaches that involved the use of classical methods, other neural architectures and the incorporation of different linguistic features.

Related papers

• Towards More Accurate Prediction of Human Empathy and Emotion in Text and Multi-turn Conversations by Combining Advanced NLP, Transformers-based Networks, and Linguistic Methodologies [0.0]
  We predict the level of empathic concern and personal distress displayed in essays. Based on the WASSA 2022 Shared Task on Empathy Detection and Emotion Classification, we implement a Feed-Forward Neural Network. As part of the final stage, these approaches have been adapted to the WASSA 2023 Shared Task on Empathy Emotion and Personality Detection in Interactions.
  arXiv  Detail & Related papers  (2024-07-26T04:01:27Z)
• Agentivit\`a e telicit\`a in GilBERTo: implicazioni cognitive [77.71680953280436]
  The goal of this study is to investigate whether a Transformer-based neural language model infers lexical semantics. The semantic properties considered are telicity (also combined with definiteness) and agentivity.
  arXiv  Detail & Related papers  (2023-07-06T10:52:22Z)
• DiPlomat: A Dialogue Dataset for Situated Pragmatic Reasoning [89.92601337474954]
  Pragmatic reasoning plays a pivotal role in deciphering implicit meanings that frequently arise in real-life conversations. We introduce a novel challenge, DiPlomat, aiming at benchmarking machines' capabilities on pragmatic reasoning and situated conversational understanding.
  arXiv  Detail & Related papers  (2023-06-15T10:41:23Z)
• Effective Cross-Task Transfer Learning for Explainable Natural Language Inference with T5 [50.574918785575655]
  We compare sequential fine-tuning with a model for multi-task learning in the context of boosting performance on two tasks. Our results show that while sequential multi-task learning can be tuned to be good at the first of two target tasks, it performs less well on the second and additionally struggles with overfitting.
  arXiv  Detail & Related papers  (2022-10-31T13:26:08Z)
• Multiview Contextual Commonsense Inference: A New Dataset and Task [40.566530682082714]
  CICEROv2 is a dataset consisting of 8,351 instances from 2,379 dialogues. It contains multiple human-written answers for each contextual commonsense inference question. We show that the inferences in CICEROv2 are more semantically diverse than other contextual commonsense inference datasets.
  arXiv  Detail & Related papers  (2022-10-06T13:08:41Z)
• Can Unsupervised Knowledge Transfer from Social Discussions Help Argument Mining? [25.43442712037725]
  We propose a novel transfer learning strategy to overcome the challenges of unsupervised, argumentative discourse-aware knowledge. We utilize argumentation-rich social discussions from the ChangeMyView subreddit as a source of unsupervised, argumentative discourse-aware knowledge. We introduce a novel prompt-based strategy for inter-component relation prediction that compliments our proposed finetuning method.
  arXiv  Detail & Related papers  (2022-03-24T06:48:56Z)
• Prediction or Comparison: Toward Interpretable Qualitative Reasoning [16.02199526395448]
  Current approaches use either semantics to transform natural language inputs into logical expressions or a "black-box" model to solve them in one step. In this work, we categorize qualitative reasoning tasks into two types: prediction and comparison. In particular, we adopt neural network modules trained in an end-to-end manner to simulate the two reasoning processes.
  arXiv  Detail & Related papers  (2021-06-04T10:27:55Z)
• Episodic Transformer for Vision-and-Language Navigation [142.6236659368177]
  This paper focuses on addressing two challenges: handling long sequence of subtasks, and understanding complex human instructions. We propose Episodic Transformer (E.T.), a multimodal transformer that encodes language inputs and the full episode history of visual observations and actions. Our approach sets a new state of the art on the challenging ALFRED benchmark, achieving 38.4% and 8.5% task success rates on seen and unseen test splits.
  arXiv  Detail & Related papers  (2021-05-13T17:51:46Z)
• Infusing Finetuning with Semantic Dependencies [62.37697048781823]
  We show that, unlike syntax, semantics is not brought to the surface by today's pretrained models. We then use convolutional graph encoders to explicitly incorporate semantic parses into task-specific finetuning.
  arXiv  Detail & Related papers  (2020-12-10T01:27:24Z)
• Multimodal Pretraining Unmasked: A Meta-Analysis and a Unified Framework of Vision-and-Language BERTs [57.74359320513427]
  Methods have been proposed for pretraining vision and language BERTs to tackle challenges at the intersection of these two key areas of AI. We study the differences between these two categories, and show how they can be unified under a single theoretical framework. We conduct controlled experiments to discern the empirical differences between five V&L BERTs.
  arXiv  Detail & Related papers  (2020-11-30T18:55:24Z)
• CS-NLP team at SemEval-2020 Task 4: Evaluation of State-of-the-art NLP Deep Learning Architectures on Commonsense Reasoning Task [3.058685580689605]
  We describe our attempt at SemEval-2020 Task 4 competition: Commonsense Validation and Explanation (ComVE) challenge. Our system uses prepared labeled textual datasets that were manually curated for three different natural language inference subtasks. For the second subtask, which is to select the reason why a statement does not make sense, we stand within the first six teams (93.7%) among 27 participants with very competitive results.
  arXiv  Detail & Related papers  (2020-05-17T13:20:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.