Papers with unsupervised adaptation
Unsupervised Enrichment of Persona-grounded Dialog with Background Stories (2021.acl-short)
Copied to clipboard
| Challenge: | Existing dialog models do not contain such narratives, so we propose a gradient-based rewriting technique to enrich dialog personas with relevant background events. |
| Approach: | They propose to use existing dialog datasets to enrich dialog responses with 'background stories' based on a gradient-based rewriting technique which encourages the generated response to be fluent with the dialog history, minimally different from the retrieved story, and consistent with the original persona. |
| Outcome: | The proposed method generates responses that are more diverse and human-like compared to outputs from existing dialog models. |
Knowledge-Aware Meta-learning for Low-Resource Text Classification (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing meta-learning techniques may not be well-suited to testing tasks when they are not well-supported by training tasks. |
| Approach: | They propose to use a meta-learning algorithm to add representations for each sentence learned from the extracted sentence-specific knowledge graph. |
| Outcome: | The proposed model is able to represent each sentence learned from the extracted knowledge graph under supervised adaptation and unsupervised adaptation settings. |
Llama2Vec: Unsupervised Adaptation of Large Language Models for Dense Retrieval (2024.acl-long)
Copied to clipboard
| Challenge: | Dense retrieval requires discriminative embeddings to represent the semantic relationship between query and document. |
| Approach: | They propose an unsupervised approach that performs unsupervised adaptation of large language models for dense retrieval. |
| Outcome: | The proposed model improves on a variety of dense retrieval benchmarks and is available on github. |
Adversarial Domain Adaptation Using Artificial Titles for Abstractive Title Generation (P19-1)
Copied to clipboard
| Challenge: | Obtaining good quality labeled data can be difficult and expensive for abstractive summarization models . authors propose the use of artificial titles for unlabeled target documents . |
| Approach: | They propose to use artificial titles and sequential training to capture grammatical style of unlabeled target domains to adapt to/from news articles and Stack Exchange posts. |
| Outcome: | The proposed techniques can boost performance for unsupervised adaptation and fine-tuning with limited target data. |
Prosody as Supervision: Bridging the Non-Verbal–Verbal for Multilingual Speech Emotion Recognition (2026.acl-long)
Copied to clipboard
| Challenge: | Existing paradigms for low-resource multilingual speech emotion recognition rely on labeled verbal speech and lack cross-lingual transfer. |
| Approach: | They propose a paralinguistic supervision paradigm for low-resource multilingual speech emotion recognition that leverages non-verbal vocalizations to exploit prosody-centric emotion cues. |
| Outcome: | The proposed framework outperforms Euclidean counter parts and strong SSL baselines in the language-based evaluation of low-resource multilingual speech emotion recognition (LRM-SER) |