Papers with auto-encoder
OodGAN: Generative Adversarial Network for Out-of-Domain Data Generation (2021.naacl-industry)
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| Challenge: | Existing models for OOD detection work with text, but they do not work directly with the text. |
| Approach: | They propose to use a sequential generative adversarial network (SeqGAN) based model to generate OOD data for a given domain automatically. |
| Outcome: | The proposed model outperforms state-of-the-art in OOD detection metrics for ROSTD and OSQ datasets. |
A new approach for fine-tuning sentence transformers for intent classification and out-of-scope detection tasks (2024.emnlp-industry)
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| Challenge: | Virtual assistants often can handle only a limited scope of intents. |
| Approach: | They propose to combine out-of-scope (OOS) rejection with intent classification . they propose to regularize cross-entropy loss with an in-scope embedding reconstruction loss . |
| Outcome: | The proposed method achieves a 1-4% improvement in the area under the precision-recall curve for rejecting out-of-sample instances without compromising intent classification performance. |
Learning to Encode Text as Human-Readable Summaries using Generative Adversarial Networks (D18-1)
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| Challenge: | a popular approach to learning data representations involves the use of an auto-encoder that compresses data into a latent-space representation without supervision. |
| Approach: | They propose to train an auto-encoder that encodes input text into human-readable sentences . they use comprehensible natural language as a latent representation of the input source text . |
| Outcome: | The proposed auto-encoder can encode input text into human-readable sentences without document-summary pairs. |
TruthX: Alleviating Hallucinations by Editing Large Language Models in Truthful Space (2024.acl-long)
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| Challenge: | Large Language Models (LLMs) sometimes produce untruthful responses despite knowing the correct knowledge. |
| Approach: | They propose an inference-time intervention method to activate the truthfulness of Large Language Models (LLMs) by editing the features within LLM’s internal representations that govern the truthful. |
| Outcome: | The proposed method improves the truthfulness of 13 advanced LLMs by an average of 20% on TruthfulQA benchmark. |
Multimodal Transformer Networks for End-to-End Video-Grounded Dialogue Systems (P19-1)
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| Challenge: | Existing work on video-grounded dialogue systems is limited by feature space and semantic information. |
| Approach: | They propose multimodal transformer networks to encode videos and incorporate information from different modalities. |
| Outcome: | The proposed system generates appropriate conversational response to queries of humans based on visual and audio aspects of a given video . it also generalizes to another multimodal visual-grounded dialogue task, and obtains promising performance. |
StoryTrans: Non-Parallel Story Author-Style Transfer with Discourse Representations and Content Enhancing (2023.acl-long)
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| Challenge: | Existing studies on text style transfer neglect long style transfer at the discourse level. |
| Approach: | They propose a model that transfers text style into target styles with learnable style embeddings . they use a mask-and-fill framework to explicitly fuse style-specific keywords into generation . |
| Outcome: | The proposed model outperforms baselines in style transfer and content preservation. |