Unsupervised Candidate Answer Extraction through Differentiable Masker-Reconstructor Model

• URL: http://arxiv.org/abs/2310.13106v1
• Date: Thu, 19 Oct 2023 19:07:08 GMT
• Title: Unsupervised Candidate Answer Extraction through Differentiable Masker-Reconstructor Model
• Authors: Zhuoer Wang, Yicheng Wang, Ziwei Zhu, James Caverlee
• Abstract summary: We propose a novel unsupervised candidate answer extraction approach that leverages the inherent structure of context passages through a Differentiable Masker-Reconstructor (DMR) Model. We benchmark a comprehensive set of supervised and unsupervised candidate answer extraction methods. We demonstrate the effectiveness of the DMR model by showing its performance is superior among unsupervised methods and comparable to supervised methods.
• Score: 21.667471025804936
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Question generation is a widely used data augmentation approach with extensive applications, and extracting qualified candidate answers from context passages is a critical step for most question generation systems. However, existing methods for candidate answer extraction are reliant on linguistic rules or annotated data that face the partial annotation issue and challenges in generalization. To overcome these limitations, we propose a novel unsupervised candidate answer extraction approach that leverages the inherent structure of context passages through a Differentiable Masker-Reconstructor (DMR) Model with the enforcement of self-consistency for picking up salient information tokens. We curated two datasets with exhaustively-annotated answers and benchmark a comprehensive set of supervised and unsupervised candidate answer extraction methods. We demonstrate the effectiveness of the DMR model by showing its performance is superior among unsupervised methods and comparable to supervised methods.

Related papers

• Pseudo-RIS: Distinctive Pseudo-supervision Generation for Referring Image Segmentation [10.958014189747356]
  We propose a new framework that automatically generates high-quality segmentation masks with their referring expressions as pseudo supervisions for referring image segmentation (RIS) Our method significantly outperforms both weakly and zero-shot SoTA methods on the RIS benchmark datasets. It also surpasses fully supervised methods in unseen domains, proving its capability to tackle the open-world challenge within RIS.
  arXiv  Detail & Related papers  (2024-07-10T07:14:48Z)
• Inverse-RLignment: Inverse Reinforcement Learning from Demonstrations for LLM Alignment [62.05713042908654]
  We introduce Alignment from Demonstrations (AfD), a novel approach leveraging high-quality demonstration data to overcome these challenges. We formalize AfD within a sequential decision-making framework, highlighting its unique challenge of missing reward signals. Practically, we propose a computationally efficient algorithm that extrapolates over a tailored reward model for AfD.
  arXiv  Detail & Related papers  (2024-05-24T15:13:53Z)
• Towards General Visual-Linguistic Face Forgery Detection [95.73987327101143]
  Deepfakes are realistic face manipulations that can pose serious threats to security, privacy, and trust. Existing methods mostly treat this task as binary classification, which uses digital labels or mask signals to train the detection model. We propose a novel paradigm named Visual-Linguistic Face Forgery Detection(VLFFD), which uses fine-grained sentence-level prompts as the annotation.
  arXiv  Detail & Related papers  (2023-07-31T10:22:33Z)
• Passage-Mask: A Learnable Regularization Strategy for Retriever-Reader Models [36.58955176223759]
  Retriever-reader models achieve competitive performance across many different NLP tasks such as open question answering and dialogue conversations. We introduce a learnable passage mask mechanism which desensitizes the impact from the top-rank retrieval passages and prevents the model from overfitting.
  arXiv  Detail & Related papers  (2022-11-02T06:39:46Z)
• Multi-Perspective Abstractive Answer Summarization [76.10437565615138]
  Community Question Answering forums contain a rich resource of answers to a wide range of questions. The goal of multi-perspective answer summarization is to produce a summary that includes all perspectives of the answer. This work introduces a novel dataset creation method to automatically create multi-perspective, bullet-point abstractive summaries.
  arXiv  Detail & Related papers  (2021-04-17T13:15:29Z)
• Discrete Reasoning Templates for Natural Language Understanding [79.07883990966077]
  We present an approach that reasons about complex questions by decomposing them to simpler subquestions. We derive the final answer according to instructions in a predefined reasoning template. We show that our approach is competitive with the state-of-the-art while being interpretable and requires little supervision.
  arXiv  Detail & Related papers  (2021-04-05T18:56:56Z)
• Cooperative Learning of Zero-Shot Machine Reading Comprehension [9.868221447090855]
  We propose a cooperative, self-play learning model for question generation and answering. We can train question generation and answering models on any textual corpora without annotation. Our model outperforms the state-of-the-art pretrained language models on standard question answering benchmarks.
  arXiv  Detail & Related papers  (2021-03-12T18:22:28Z)
• Abstractive Query Focused Summarization with Query-Free Resources [60.468323530248945]
  In this work, we consider the problem of leveraging only generic summarization resources to build an abstractive QFS system. We propose Marge, a Masked ROUGE Regression framework composed of a novel unified representation for summaries and queries. Despite learning from minimal supervision, our system achieves state-of-the-art results in the distantly supervised setting.
  arXiv  Detail & Related papers  (2020-12-29T14:39:35Z)
• Context Modeling with Evidence Filter for Multiple Choice Question Answering [18.154792554957595]
  Multiple-Choice Question Answering (MCQA) is a challenging task in machine reading comprehension. The main challenge is to extract "evidence" from the given context that supports the correct answer. Existing work tackles this problem by annotated evidence or distant supervision with rules which overly rely on human efforts. We propose a simple yet effective approach termed evidence filtering to model the relationships between the encoded contexts.
  arXiv  Detail & Related papers  (2020-10-06T11:53:23Z)
• Subspace Clustering for Action Recognition with Covariance Representations and Temporal Pruning [20.748083855677816]
  This paper tackles the problem of human action recognition, defined as classifying which action is displayed in a trimmed sequence, from skeletal data. We propose a novel subspace clustering method, which exploits covariance matrix to enhance the action's discriminability and a timestamp pruning approach that allow us to better handle the temporal dimension of the data.
  arXiv  Detail & Related papers  (2020-06-21T14:44:03Z)
• A Revised Generative Evaluation of Visual Dialogue [80.17353102854405]
  We propose a revised evaluation scheme for the VisDial dataset. We measure consensus between answers generated by the model and a set of relevant answers. We release these sets and code for the revised evaluation scheme as DenseVisDial.
  arXiv  Detail & Related papers  (2020-04-20T13:26:45Z)

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.