Papers with human-annotated training data
Zero-shot Fact Verification by Claim Generation (2021.acl-short)
Copied to clipboard
| Challenge: | Existing methods for fact verification require large datasets, which can be expensive. |
| Approach: | They propose a framework for training a robust fact verification model by using automatically generated claims that can be supported, refuted, or unverifiable from evidence from Wikipedia. |
| Outcome: | The proposed framework reduces the demand for human-annotated training data and improves a model's F1 from 50% to 77%, equivalent in performance to 2K+ manually-curated examples. |
SynthDST: Synthetic Data is All You Need for Few-Shot Dialog State Tracking (2024.eacl-long)
Copied to clipboard
| Challenge: | In-context learning with Large Language Models (LLMs) is a promising avenue of research in Dialog State Tracking (DST). |
| Approach: | They propose a data generation framework tailored for Dialog State Tracking that uses large language models to synthesize natural, coherent, and free-flowing dialogues with DST annotations. |
| Outcome: | The proposed framework improves joint goal accuracy by 4-5% over the zero-shot baseline on MultiWOZ 2.1 and 2.4. |
Unsupervised Natural Language Inference Using PHL Triplet Generation (2022.findings-acl)
Copied to clipboard
| Challenge: | In some cases, training samples may not be available or collecting them could be time-consuming and resource-intensive. |
| Approach: | They propose a procedural approach that leverages sentence transformations to collect PHL triplets for training NLI models. |
| Outcome: | The proposed model outperforms existing models on several NLI benchmarks with a set of sentence transformations. |
Unsupervised Multi-hop Question Answering by Question Generation (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing training data for multi-hop question answering (QA) is time-consuming and resource-intensive. |
| Approach: | They propose an unsupervised framework that generates human-like multi-hop training data from homogeneous and heterogeneously data sources. |
| Outcome: | The proposed framework achieves 61% and 83% of the supervised learning performance for the HybridQA and HotpotQA datasets. |