Papers with resource-demanding
The Turing Quest: Can Transformers Make Good NPCs? (2023.acl-srw)
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| Challenge: | Pre-trained Transformer-based language models have demonstrated impressive conversational abilities, but their use in real-world applications remains unexplored. |
| Approach: | They propose a pipeline for automatic construction of NPC scripts using Transformer-based believable scripts for a variety of game genres and specifications. |
| Outcome: | The proposed pipeline generates scripts that fool judges in a variety of game genres and contexts, and can be easily compared to human-written scripts. |
Progressively Pretrained Dense Corpus Index for Open-Domain Question Answering (2021.eacl-main)
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| Challenge: | Existing open-domain question answering systems are insufficient to capture deep semantic matching that goes beyond lexical overlaps. |
| Approach: | They propose a sample-efficient method to pretrain the paragraph encoder using an existing pretraining model instead of heuristically created pseudo question-paragraph pairs. |
| Outcome: | The proposed method outperforms a strong dense retrieval baseline that uses 6 times more computation for training. |
Improving Machine Reading Comprehension with General Reading Strategies (N19-1)
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| Challenge: | Recent studies have shown that reading strategies improve comprehension levels for readers lacking adequate prior knowledge. |
| Approach: | They propose three general strategies to improve machine reading comprehension (MRC) by fine-tuning a pre-trained model with strategies and a target task. |
| Outcome: | The proposed models improve non-extractive machine reading comprehension (MRC) on the largest general domain multiple-choice dataset RACE. |
IP-Dialog: Evaluating Implicit Personalization in Dialogue Systems with Synthetic Data (2025.findings-emnlp)
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| Challenge: | Traditional data generation methods are labor-intensive, resource-demanding, and raise privacy concerns. |
| Approach: | They propose an automatic synthetic data generation approach and introduce the **I**mplicit **P**ersonalized **Dialog**ue benchmark along with a training dataset, covering 10 tasks and 12 user attribute types. |
| Outcome: | The proposed approach incorporates the **Implicit **P**ersonalized **Dialog**ue benchmark along with a training dataset, covering 10 tasks and 12 user attribute types. |