Learning to Ask Conversational Questions by Optimizing Levenshtein Distance

• URL: http://arxiv.org/abs/2106.15903v1
• Date: Wed, 30 Jun 2021 08:44:19 GMT
• Title: Learning to Ask Conversational Questions by Optimizing Levenshtein Distance
• Authors: Zhongkun Liu, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Maarten de Rijke, Ming Zhou
• Abstract summary: We introduce a Reinforcement Iterative Sequence Editing (RISE) framework that optimize the minimum Levenshtein distance (MLD) through explicit editing actions. RISE is able to pay attention to tokens that are related to conversational characteristics. Experimental results on two benchmark datasets show that RISE significantly outperforms state-of-the-art methods.
• Score: 83.53855889592734
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Conversational Question Simplification (CQS) aims to simplify self-contained questions into conversational ones by incorporating some conversational characteristics, e.g., anaphora and ellipsis. Existing maximum likelihood estimation (MLE) based methods often get trapped in easily learned tokens as all tokens are treated equally during training. In this work, we introduce a Reinforcement Iterative Sequence Editing (RISE) framework that optimizes the minimum Levenshtein distance (MLD) through explicit editing actions. RISE is able to pay attention to tokens that are related to conversational characteristics. To train RISE, we devise an Iterative Reinforce Training (IRT) algorithm with a Dynamic Programming based Sampling (DPS) process to improve exploration. Experimental results on two benchmark datasets show that RISE significantly outperforms state-of-the-art methods and generalizes well on unseen data.

Related papers

• Enhancing RAG with Active Learning on Conversation Records: Reject Incapables and Answer Capables [17.76687504479359]
  Retrieval-augmented generation (RAG) is a key technique for leveraging external knowledge and reducing hallucinations in large language models (LLMs) This paper proposes using the vast amount of conversations from widespread LLM usage to build high-quality datasets. We introduce AL4RAG, which uses active learning to select the most suitable conversation samples for annotation.
  arXiv  Detail & Related papers  (2025-02-13T08:42:29Z)
• Token Assorted: Mixing Latent and Text Tokens for Improved Language Model Reasoning [44.84219266082269]
  Large Language Models (LLMs) excel at reasoning and planning when trained on chainof-thought (CoT) data. We propose a hybrid representation of the reasoning process, where we partially abstract away the initial reasoning steps using latent discrete tokens.
  arXiv  Detail & Related papers  (2025-02-05T15:33:00Z)
• A Systematic Examination of Preference Learning through the Lens of Instruction-Following [83.71180850955679]
  We use a novel synthetic data generation pipeline to generate 48,000 instruction unique-following prompts. With our synthetic prompts, we use two preference dataset curation methods - rejection sampling (RS) and Monte Carlo Tree Search (MCTS) Experiments reveal that shared prefixes in preference pairs, as generated by MCTS, provide marginal but consistent improvements. High-contrast preference pairs generally outperform low-contrast pairs; however, combining both often yields the best performance.
  arXiv  Detail & Related papers  (2024-12-18T15:38:39Z)
• Generalization v.s. Memorization: Tracing Language Models' Capabilities Back to Pretraining Data [76.90128359866462]
  We introduce an extended concept of memorization, distributional memorization, which measures the correlation between the output probabilities and the pretraining data frequency. We show that memorization plays a larger role in simpler, knowledge-intensive tasks, while generalization is the key for harder, reasoning-based tasks.
  arXiv  Detail & Related papers  (2024-07-20T21:24:40Z)
• Instruction Position Matters in Sequence Generation with Large Language Models [67.87516654892343]
  Large language models (LLMs) are capable of performing conditional sequence generation tasks, such as translation or summarization. We propose enhancing the instruction-following capability of LLMs by shifting the position of task instructions after the input sentences.
  arXiv  Detail & Related papers  (2023-08-23T12:36:57Z)
• Guiding Large Language Models via Directional Stimulus Prompting [114.84930073977672]
  We introduce Directional Stimulus Prompting, a novel framework for guiding black-box large language models (LLMs) toward specific desired outputs. Instead of directly adjusting LLMs, our method employs a small tunable policy model to generate an auxiliary directional stimulus prompt for each input instance.
  arXiv  Detail & Related papers  (2023-02-22T17:44:15Z)
• Momentum Contrastive Pre-training for Question Answering [54.57078061878619]
  MCROSS introduces a momentum contrastive learning framework to align the answer probability between cloze-like and natural query-passage sample pairs. Our method achieves noticeable improvement compared with all baselines in both supervised and zero-shot scenarios.
  arXiv  Detail & Related papers  (2022-12-12T08:28:22Z)
• KECP: Knowledge Enhanced Contrastive Prompting for Few-shot Extractive Question Answering [28.18555591429343]
  We propose a novel framework named Knowledge Enhanced Contrastive Prompt-tuning (KECP) Instead of adding pointer heads to PLMs, we transform the task into a non-autoregressive Masked Language Modeling (MLM) generation problem. Our method consistently outperforms state-of-the-art approaches in few-shot settings by a large margin.
  arXiv  Detail & Related papers  (2022-05-06T08:31:02Z)
• Making Pre-trained Language Models End-to-end Few-shot Learners with Contrastive Prompt Tuning [41.15017636192417]
  We present CP-Tuning, the first end-to-end Contrastive Prompt Tuning framework for fine-tuning Language Models. It is integrated with the task-invariant continuous prompt encoding technique with fully trainable prompt parameters. Experiments over a variety of language understanding tasks used in IR systems and different PLMs show that CP-Tuning outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-04-01T02:24:24Z)

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.