Papers with SocialIQA
TSGP: Two-Stage Generative Prompting for Unsupervised Commonsense Question Answering (2022.findings-emnlp)
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| Challenge: | Existing studies focus on acquiring relevant knowledge by retrieving external knowledge bases and fine-tuning pre-trained models. |
| Approach: | They propose a two-stage prompt-based unsupervised commonsense question answering framework that leverages implicit knowledge stored in PrLMs to generate knowledge for questions with unlimited types and possible candidate answers independent of specified choices. |
| Outcome: | The proposed framework significantly improves the reasoning ability of language models in unsupervised settings. |
On Curriculum Learning for Commonsense Reasoning (2022.naacl-main)
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| Challenge: | Recent research suggests that data order can have a significant impact on the performance of finetuned models for natural language understanding. |
| Approach: | They use paced curriculum learning to rank data and sample training mini-batches with increasing levels of difficulty during finetuning. |
| Outcome: | The proposed model improves performance for socialIQA, CosmosQA, CODAH, HellaSwag, WinoGrande in both tuning settings. |
ArT: All-round Thinker for Unsupervised Commonsense Question Answering (2022.coling-1)
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| Challenge: | Existing work on commonsense QA requires labeled training data for its success . existing work relies on large-scale in-domain or out-of-domain labeles or fails to generate knowledge of high quality in a general way. |
| Approach: | They propose an approach to commonsense question-answering (QA) that takes association during knowledge generation. |
| Outcome: | The proposed model outperforms existing models on commonsense QA benchmarks. |
Identify, Align, and Integrate: Matching Knowledge Graphs to Commonsense Reasoning Tasks (2021.eacl-main)
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| Challenge: | Empirically, we investigate KG matches for the SocialIQA, Physical IQA, and MCScript2.0 datasets with 3 diverse KGs: ATOMIC (SIQA), ConceptNet (Speer et al., 2017), and an automatically constructed instructional KG based on WikiHow (Ostermann e., 2019b). |
| Approach: | They propose a method to assess how well a candidate KG can fill in knowledge gaps for a given task by using commonsense probes. |
| Outcome: | Empirically, we show that the proposed KG-to-task match is a good match for socialIQA, physical IQA, and MCScript2.0 datasets with 3 diverse KGs: ATOMIC, ConceptNet, and an instructional KG based on WikiHow. |
GraDA: Graph Generative Data Augmentation for Commonsense Reasoning (2022.coling-1)
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| Challenge: | Recent advances in commonsense reasoning have been fueled by the availability of large-scale human annotated datasets. |
| Approach: | They propose a graph-generative data augmentation framework to synthesize factual data samples from knowledge graphs for commonsense reasoning. |
| Outcome: | The proposed framework improves SocialIQA, CODAH, HellaSwag and CommonsenseQA . it also performs well for generative tasks like ProtoQA proving its robustness to adversaries . |