| Challenge: | a gap exists in natural language understanding of commands between humans and machines. |
| Approach: | They propose a method for commonsense inference to transform high-level commands into action commands for robotic systems to execute. |
| Outcome: | The proposed method allows to build a knowledge base that consists of a large set of commonsense inferences. |
Similar Papers
Commonsense Reasoning for Natural Language Processing (2020.acl-tutorials)
Copied to clipboard
| Challenge: | In this tutorial, we will outline the various types of commonsense knowledge and discuss techniques to gather and represent commonsence knowledge. |
| Approach: | This tutorial will provide researchers with the critical foundations and recent advances in commonsense representation and reasoning. |
| Outcome: | This tutorial will outline the various types of commonsense and discuss techniques to gather and represent commonsence knowledge while highlighting the challenges specific to this type of knowledge (e.g., reporting bias). |
Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing (D19-60)
Copied to clipboard
| Challenge: | Workshop on Commonsense Inference in Natural Language Processing focuses on commonsense knowledge representation and application in NLP tasks. |
| Approach: | COIN is a workshop on commonsense inference in natural language processing . workshop included two shared tasks on reading comprehension using commonsensense knowledge . |
| Outcome: | the workshop focused on modeling commonsense knowledge and commonsensing in natural language processing tasks. |
Improving Natural Language Interaction with Robots Using Advice (N19-1)
Copied to clipboard
| Challenge: | Recent studies focus on learning models for physically grounded language understanding tasks such as the blocks world domain. |
| Approach: | They propose a protocol for including advice, high-level observations about the task, which can help constrain the agent’s prediction. |
| Outcome: | The proposed approach can be extended to include advice, high-level observations about the task, and reduce the effort involved in supplying the advice. |
Language in a (Search) Box: Grounding Language Learning in Real-World Human-Machine Interaction (2021.naacl-main)
Copied to clipboard
| Challenge: | Scholarly work in this area uses toy worlds and synthetic linguistic data, but grounded language learning offers several practical and scientific advantages. |
| Approach: | They propose to model teacher-learner dynamics through natural interactions occurring between users and search engines. |
| Outcome: | The proposed model is better than non-grounded models on compositionality and zero-shot inference tasks. |
Improving Unsupervised Commonsense Reasoning Using Knowledge-Enabled Natural Language Inference (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Recent methods based on pre-trained language models have shown strong supervised performance on commonsense reasoning. |
| Approach: | They propose to use a common framework to solve commonsense reasoning tasks using a dataset from NLI. |
| Outcome: | The proposed method achieves state-of-the-art unsupervised performance on two commonsense reasoning tasks. |
Grounded Semantic Role Labelling from Synthetic Multimodal Data for Situated Robot Commands (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing symbolic parsers lack flexibility to operate in complex, dynamic environments. |
| Approach: | They propose a framework that combines frame semantics with perceptual grounding to enable robots to interpret commands via multimodal logical forms. |
| Outcome: | The proposed framework produces over 11,000 image-command pairs and lowers the cost of manual parsers. |
tagE: Enabling an Embodied Agent to Understand Human Instructions (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing systems for natural language understanding (NLU) are limited due to the inherent ambiguity and incompleteness inherent in natural language. |
| Approach: | They propose a system to extract tasks from natural language instructions and map them to robots' established collection of skills. |
| Outcome: | The proposed system outperforms baseline models in the training and evaluation of a dataset featuring complex instructions. |
Commonsense Knowledge Transfer for Pre-trained Language Models (2023.findings-acl)
Copied to clipboard
| Challenge: | Recent advances in pre-trained language models have transformed the landscape of natural language processing. |
| Approach: | They propose a framework to transfer commonsense knowledge stored in a neural commonsensing model to a general-purpose pre-trained language model. |
| Outcome: | Empirical results show that the proposed framework improves the model’s performance on downstream tasks that require commonsense reasoning. |
Natural Language Annotations for Reasoning about Program Semantics (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Xu et al., 2022) and Tafjord eet . al. 2021) have shown that programming assistants can explain their work by grounding natural language inference in code. |
| Approach: | They propose a dataset and protocol for annotating programs with natural language predicates at a finer granularity than code comments without relying on internal compiler representations. |
| Outcome: | The proposed method can be used to ground natural language inference in code without static analysis and without internal compiler representations. |
Learning Language through Grounding (2025.naacl-tutorial)
Copied to clipboard
| Challenge: | This tutorial provides a historical overview of grounding and discusses its use in computational linguistics and in computational language processing. |
| Approach: | They introduce the concept of grounding and discuss future directions and open challenges . they will delve into recent progress in learning lexical semantics, syntax, and complex meanings through various forms of ground. |
| Outcome: | This course will provide an overview of the field of grounding and discuss future directions and challenges related to large language models and scaling. |