ACCENT: An Automatic Event Commonsense Evaluation Metric for Open-Domain Dialogue Systems (2023.acl-long)
Copied to clipboard
| Challenge: | evaluating commonsense in dialogue systems remains an open challenge . despite the success of open-domain dialogue systems, systems struggle to produce commonsensical responses as humans do. |
| Approach: | They propose an event commonsense evaluation metric empowered by commonsensence knowledge bases. |
| Outcome: | The proposed metric achieves higher correlations with human judgments than baselines. |
Similar Papers
Towards Holistic and Automatic Evaluation of Open-Domain Dialogue Generation (2020.acl-main)
Copied to clipboard
| Challenge: | Existing methods of open-domain dialogue evaluation are labor-intensive and inefficient. |
| Approach: | They propose to use open-domain dialogues to evaluate different aspects of dialogues using holistic evaluation metrics. |
| Outcome: | The proposed metrics show strong correlations with human judgments. |
Event2Mind: Commonsense Inference on Events, Intents, and Reactions (P18-1)
Copied to clipboard
| Challenge: | Using a crowdsourced corpus of 25,000 event phrases, we construct a new task that uses commonsense reasoning to reason about the likely intents and reactions of the event participants. |
| Approach: | They construct a crowdsourced corpus of 25,000 event phrases and use them to construct 'commonsense inference' they demonstrate that neural encoder-decoder models can compose embedding representations of previously unseen events and reason about the likely intents and reactions of the event participants. |
| Outcome: | The proposed task can be used to uncover implicit gender inequality in movie scripts. |
Deconstruct to Reconstruct a Configurable Evaluation Metric for Open-Domain Dialogue Systems (2020.coling-main)
Copied to clipboard
| Challenge: | Existing evaluation metrics are not designed to cope with this flexibility. |
| Approach: | They propose to group the qualities into three groups to obtain a single metric called USL-H. |
| Outcome: | The proposed metric achieves good correlations with human judgment and maintains its configurability towards different aspects and metrics. |
A Method for Building a Commonsense Inference Dataset based on Basic Events (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to acquire commonsense are limited by the general-purpose language models. |
| Approach: | They propose a method for building a commonsense inference dataset using crowdsourcing and automatic extraction from a corpus. |
| Outcome: | The proposed method can solve 104k commonsense inference problems in a Japanese corpus with high accuracy, but low bias. |
Elaboration-Generating Commonsense Question Answering at Scale (2023.acl-long)
Copied to clipboard
| Challenge: | elaborations are generated using language models that generate background knowledge that helps improve performance . human evaluations show that the quality of the generated ellaborations is high . |
| Approach: | They propose to finetune smaller language models to generate useful intermediate context . they compare a language model with an answer predictor and generate elaborations . human evaluations show that the quality of the generated ellaborations is high . |
| Outcome: | The proposed framework outperforms other models on commonsense questions on four commons sense benchmarks. |
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). |
Complex Reasoning over Logical Queries on Commonsense Knowledge Graphs (2024.acl-long)
Copied to clipboard
| Challenge: | Currently, language models struggle to generate commonsense inferences for complex tasks due to data scarcity and the difficulty of reasoning over multiple pieces of information. |
| Approach: | They propose a dataset to generate commonsense inferences from commonsensible data . they use a commonsence knowledge graph to extract and form questions from existing commonseense knowledge graphs. |
| Outcome: | The proposed dataset improves the ability of language models to reason about complex events without expensive human annotations. |
Event Representation Learning Enhanced with External Commonsense Knowledge (D19-1)
Copied to clipboard
| Challenge: | Existing methods to learn event representations from text lack commonsense knowledge about the intents and emotions of event participants. |
| Approach: | They propose to leverage external commonsense knowledge about the intent and sentiment of the event to learn distributed representations for structured events from text. |
| Outcome: | The proposed model improves on hard similarity tasks and yields more precise inferences on subsequent events under given contexts. |
CRoW: Benchmarking Commonsense Reasoning in Real-World Tasks (2023.emnlp-main)
Copied to clipboard
| Challenge: | Recent efforts in natural language processing (NLP) commonsense reasoning research have produced a number of new datasets and benchmarks. |
| Approach: | They propose a manually-curated, multi-task benchmark that evaluates models' ability to apply commonsense reasoning in the context of six real-world NLP tasks. |
| Outcome: | The proposed benchmark evaluates the ability of models to apply commonsense reasoning in the context of six real-world NLP tasks. |
Leveraging Explicit Reasoning for Inference Integration in Commonsense-Augmented Dialogue Models (2025.coling-main)
Copied to clipboard
| Challenge: | Existing approaches to commonsense-augmented dialogue rely on implicit reasoning to integrate commonsensense inferences during response generation. |
| Approach: | They propose to separate commonsense reasoning into explicit steps for generating, selecting, and integrating commonsensense into dialogue responses. |
| Outcome: | The proposed model infers commonsense knowledge from dialogue contexts to improve response quality and naturalness of dialogue interactions. |