Papers with generation procedure
Semantic Diversity in Dialogue with Natural Language Inference (2022.naacl-main)
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| Challenge: | Existing methods to measure diversity of chitchat model responses have been proposed to measure iteratively. |
| Approach: | They propose a metric which uses Natural Language Inference to measure the semantic diversity of a set of model responses for a conversation. |
| Outcome: | The proposed metric improves the diversity of a sampled set of responses using a new generation procedure called Diversity Threshold Generation. |
Why Exposure Bias Matters: An Imitation Learning Perspective of Error Accumulation in Language Generation (2022.findings-acl)
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| Challenge: | Current language generation models suffer from issues such as repetition, incoherence, and hallucinations . |
| Approach: | They propose to analyze exposure bias from an imitation learning perspective and prove it is a problem . they show that exposure bias leads to an accumulation of errors during generation . |
| Outcome: | The proposed model fails to capture errors during generation and poor generation quality. |
A Predicate-Function-Argument Annotation of Natural Language for Open-Domain Information eXpression (2020.emnlp-main)
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| Challenge: | Existing OIE (Open Information Extraction) algorithms are redundant and not reusable. |
| Approach: | They propose a pipeline where an Open-domain Information eXpression task provides a platform for all OIE strategies. |
| Outcome: | The proposed pipeline provides a platform for all OIE strategies. |
Universal Information Extraction with Meta-Pretrained Self-Retrieval (2023.findings-acl)
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Xin Cong, Bowen Yu, Mengcheng Fang, Tingwen Liu, Haiyang Yu, Zhongkai Hu, Fei Huang, Yongbin Li, Bin Wang
| Challenge: | Existing methods for IE are task-specific, resulting in specialized and isolated approaches for different tasks. |
| Approach: | They propose a method to retrieve task-specific knowledge from pretrained language models to enhance universal IE by using a Meta-Pretraining Algorithm. |
| Outcome: | The proposed method achieves the new state-of-the-art on 4 IE tasks, 12 datasets under fully-supervised, low-resource and few-shot scenarios. |
Incorporating Background Knowledge into Video Description Generation (D18-1)
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| Challenge: | Existing methods for video captioning focus on generating generic descriptions that lack contextual knowledge. |
| Approach: | They propose a method that uses video meta-data to retrieve topically related news documents for a video and extracts the events and named entities from these documents. |
| Outcome: | The proposed model is based on a news video dataset and is evaluated on it. |
Reward-Augmented Decoding: Efficient Controlled Text Generation With a Unidirectional Reward Model (2023.emnlp-main)
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| Challenge: | Large language models generate text that is problematic or lacks a desired attribute. |
| Approach: | They propose a text generation procedure that uses a small unidirectional reward model to encourage a language model to generate text with certain properties. |
| Outcome: | The proposed procedure outperforms prior weighted decoding methods and matches state-of-the-art techniques that require additional training. |
Partially-Aligned Data-to-Text Generation with Distant Supervision (2020.emnlp-main)
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| Challenge: | Using partially-aligned data is an alternative way of solving the dataset scarcity problem. |
| Approach: | They propose a task to generate human-readable text for describing some given structured data enabling more interpretability. |
| Outcome: | The proposed framework outperforms baseline models and validates the feasibility of using partially-aligned data. |
KRLS: Improving End-to-End Response Generation in Task Oriented Dialog with Reinforced Keywords Learning (2023.emnlp-main)
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| Challenge: | Task-oriented dialogs (TOD) require a model to generate a response that optimizes for task-related metrics. |
| Approach: | They propose a faster generation procedure that samples from independent next-word distributions and introduce a fine-grained reward function to help the model focus on learning key information in a dialog. |
| Outcome: | The proposed algorithm achieves state-of-the-art performance on an offline task with 15% training time reduction compared to a standard RL algorithm using auto-regressive generation. |