Improved and Efficient Conversational Slot Labeling through Question Answering

• URL: http://arxiv.org/abs/2204.02123v1
• Date: Tue, 5 Apr 2022 11:34:35 GMT
• Title: Improved and Efficient Conversational Slot Labeling through Question Answering
• Authors: Gabor Fuisz, Ivan Vuli\'c, Samuel Gibbons, Inigo Casanueva, Pawe{\l} Budzianowski
• Abstract summary: Transformer-based pretrained language models (PLMs) offer unmatched performance across the majority of natural language understanding (NLU) tasks. We focus on modeling and studying textitslot labeling (SL), a crucial component of NLU for dialog, through the QA optics. We demonstrate how QA-tuned PLMs can be applied to the SL task, reaching new state-of-the-art performance.
• Score: 48.670822631047635
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Transformer-based pretrained language models (PLMs) offer unmatched performance across the majority of natural language understanding (NLU) tasks, including a body of question answering (QA) tasks. We hypothesize that improvements in QA methodology can also be directly exploited in dialog NLU; however, dialog tasks must be \textit{reformatted} into QA tasks. In particular, we focus on modeling and studying \textit{slot labeling} (SL), a crucial component of NLU for dialog, through the QA optics, aiming to improve both its performance and efficiency, and make it more effective and resilient to working with limited task data. To this end, we make a series of contributions: 1) We demonstrate how QA-tuned PLMs can be applied to the SL task, reaching new state-of-the-art performance, with large gains especially pronounced in such low-data regimes. 2) We propose to leverage contextual information, required to tackle ambiguous values, simply through natural language. 3) Efficiency and compactness of QA-oriented fine-tuning are boosted through the use of lightweight yet effective adapter modules. 4) Trading-off some of the quality of QA datasets for their size, we experiment with larger automatically generated QA datasets for QA-tuning, arriving at even higher performance. Finally, our analysis suggests that our novel QA-based slot labeling models, supported by the PLMs, reach a performance ceiling in high-data regimes, calling for more challenging and more nuanced benchmarks in future work.

Related papers

• Enhancing Question Answering Precision with Optimized Vector Retrieval and Instructions [1.2425910171551517]
  Question-answering (QA) is an important application of Information Retrieval (IR) and language models. We propose an innovative approach to improve QA task performances by integrating optimized vector retrievals and instruction methodologies.
  arXiv  Detail & Related papers  (2024-11-01T21:14:04Z)
• Towards Efficient Visual-Language Alignment of the Q-Former for Visual Reasoning Tasks [8.921189024320919]
  We investigate the effectiveness of parameter efficient fine-tuning (PEFT) the Q-Former. Applying PEFT to the Q-Former achieves full fine-tuning using under 2% of the trainable parameters. Our findings reveal that the self-attention layers are noticeably more important in perceptual visual-language reasoning tasks.
  arXiv  Detail & Related papers  (2024-10-12T10:51:05Z)
• IDEAL: Leveraging Infinite and Dynamic Characterizations of Large Language Models for Query-focused Summarization [59.06663981902496]
  Query-focused summarization (QFS) aims to produce summaries that answer particular questions of interest, enabling greater user control and personalization. We investigate two indispensable characteristics that the LLMs-based QFS models should be harnessed, Lengthy Document Summarization and Efficiently Fine-grained Query-LLM Alignment. These innovations pave the way for broader application and accessibility in the field of QFS technology.
  arXiv  Detail & Related papers  (2024-07-15T07:14:56Z)
• CuriousLLM: Elevating Multi-Document QA with Reasoning-Infused Knowledge Graph Prompting [0.0]
  We improve over a novel approach called Knowledge Graph Prompting (KGP), which combines knowledge graphs with a LLM-based agent to improve reasoning and search accuracy. We propose a reasoning-infused LLM agent to enhance this framework. This agent mimics human curiosity to ask follow-up questions to more efficiently navigate the search.
  arXiv  Detail & Related papers  (2024-04-13T20:43:46Z)
• Graph Guided Question Answer Generation for Procedural Question-Answering [29.169773816553153]
  We introduce a method for generating exhaustive and high-quality training data for task-specific question answering (QA) models. Key technological enabler is a novel mechanism for automatic question-answer generation from procedural text. We show that small models trained with our data achieve excellent performance on the target QA task, even exceeding that of GPT3 and ChatGPT.
  arXiv  Detail & Related papers  (2024-01-24T17:01:42Z)
• Long-Tailed Question Answering in an Open World [46.67715607552547]
  We define Open Long-Tailed QA (OLTQA) as learning from long-tailed distributed data. We propose an OLTQA model that encourages knowledge sharing between head, tail and unseen tasks. On a large-scale OLTQA dataset, our model consistently outperforms the state-of-the-art.
  arXiv  Detail & Related papers  (2023-05-11T04:28:58Z)
• Parameter-Efficient Abstractive Question Answering over Tables or Text [60.86457030988444]
  A long-term ambition of information seeking QA systems is to reason over multi-modal contexts and generate natural answers to user queries. Memory intensive pre-trained language models are adapted to downstream tasks such as QA by fine-tuning the model on QA data in a specific modality like unstructured text or structured tables. To avoid training such memory-hungry models while utilizing a uniform architecture for each modality, parameter-efficient adapters add and train small task-specific bottle-neck layers between transformer layers.
  arXiv  Detail & Related papers  (2022-04-07T10:56:29Z)
• Learning to Perturb Word Embeddings for Out-of-distribution QA [55.103586220757464]
  We propose a simple yet effective DA method based on a noise generator, which learns to perturb the word embedding of the input questions and context without changing their semantics. We validate the performance of the QA models trained with our word embedding on a single source dataset, on five different target domains. Notably, the model trained with ours outperforms the model trained with more than 240K artificially generated QA pairs.
  arXiv  Detail & Related papers  (2021-05-06T14:12:26Z)
• Generating Diverse and Consistent QA pairs from Contexts with Information-Maximizing Hierarchical Conditional VAEs [62.71505254770827]
  We propose a conditional variational autoencoder (HCVAE) for generating QA pairs given unstructured texts as contexts. Our model obtains impressive performance gains over all baselines on both tasks, using only a fraction of data for training.
  arXiv  Detail & Related papers  (2020-05-28T08:26:06Z)
• Template-Based Question Generation from Retrieved Sentences for Improved Unsupervised Question Answering [98.48363619128108]
  We propose an unsupervised approach to training QA models with generated pseudo-training data. We show that generating questions for QA training by applying a simple template on a related, retrieved sentence rather than the original context sentence improves downstream QA performance.
  arXiv  Detail & Related papers  (2020-04-24T17:57:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.