Plot Retrieval as an Assessment of Abstract Semantic Association

• URL: http://arxiv.org/abs/2311.01666v1
• Date: Fri, 3 Nov 2023 02:02:43 GMT
• Title: Plot Retrieval as an Assessment of Abstract Semantic Association
• Authors: Shicheng Xu, Liang Pang, Jiangnan Li, Mo Yu, Fandong Meng, Huawei Shen, Xueqi Cheng, Jie Zhou
• Abstract summary: Text pairs in Plot Retrieval have less word overlap and more abstract semantic association. Plot Retrieval can be the benchmark for further research on the semantic association modeling ability of IR models.
• Score: 131.58819293115124
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Retrieving relevant plots from the book for a query is a critical task, which can improve the reading experience and efficiency of readers. Readers usually only give an abstract and vague description as the query based on their own understanding, summaries, or speculations of the plot, which requires the retrieval model to have a strong ability to estimate the abstract semantic associations between the query and candidate plots. However, existing information retrieval (IR) datasets cannot reflect this ability well. In this paper, we propose Plot Retrieval, a labeled dataset to train and evaluate the performance of IR models on the novel task Plot Retrieval. Text pairs in Plot Retrieval have less word overlap and more abstract semantic association, which can reflect the ability of the IR models to estimate the abstract semantic association, rather than just traditional lexical or semantic matching. Extensive experiments across various lexical retrieval, sparse retrieval, dense retrieval, and cross-encoder methods compared with human studies on Plot Retrieval show current IR models still struggle in capturing abstract semantic association between texts. Plot Retrieval can be the benchmark for further research on the semantic association modeling ability of IR models.

Related papers

• DiSCo Meets LLMs: A Unified Approach for Sparse Retrieval and Contextual Distillation in Conversational Search [19.694957365385896]
  Conversational Search (CS) is the task of retrieving relevant documents from a corpus within a conversational context. Current methods address this by distilling embeddings from human-rewritten queries to learn the context modeling task. We propose a new distillation method, as a relaxation of the previous objective, unifying retrieval and context modeling.
  arXiv  Detail & Related papers  (2024-10-18T17:03:17Z)
• Hierarchical Indexing for Retrieval-Augmented Opinion Summarization [60.5923941324953]
  We propose a method for unsupervised abstractive opinion summarization that combines the attributability and scalability of extractive approaches with the coherence and fluency of Large Language Models (LLMs) Our method, HIRO, learns an index structure that maps sentences to a path through a semantically organized discrete hierarchy. At inference time, we populate the index and use it to identify and retrieve clusters of sentences containing popular opinions from input reviews.
  arXiv  Detail & Related papers  (2024-03-01T10:38:07Z)
• Beyond Semantics: Learning a Behavior Augmented Relevance Model with Self-supervised Learning [25.356999988217325]
  Relevance modeling aims to locate desirable items for corresponding queries. auxiliary query-item interactions extracted from user historical behavior data could provide hints to reveal users' search intents further. Our model builds multi-level co-attention for distilling coarse-grained and fine-grained semantic representations from both neighbor and target views.
  arXiv  Detail & Related papers  (2023-08-10T06:52:53Z)
• Description-Based Text Similarity [59.552704474862004]
  We identify the need to search for texts based on abstract descriptions of their content. We propose an alternative model that significantly improves when used in standard nearest neighbor search.
  arXiv  Detail & Related papers  (2023-05-21T17:14:31Z)
• ReSel: N-ary Relation Extraction from Scientific Text and Tables by Learning to Retrieve and Select [53.071352033539526]
  We study the problem of extracting N-ary relations from scientific articles. Our proposed method ReSel decomposes this task into a two-stage procedure. Our experiments on three scientific information extraction datasets show that ReSel outperforms state-of-the-art baselines significantly.
  arXiv  Detail & Related papers  (2022-10-26T02:28:02Z)
• Incorporating Relevance Feedback for Information-Seeking Retrieval using Few-Shot Document Re-Ranking [56.80065604034095]
  We introduce a kNN approach that re-ranks documents based on their similarity with the query and the documents the user considers relevant. To evaluate our different integration strategies, we transform four existing information retrieval datasets into the relevance feedback scenario.
  arXiv  Detail & Related papers  (2022-10-19T16:19:37Z)
• Abstract, Rationale, Stance: A Joint Model for Scientific Claim Verification [18.330265729989843]
  We propose an approach, named as ARSJoint, that jointly learns the modules for the three tasks with a machine reading comprehension framework. The experimental results on the benchmark dataset SciFact show that our approach outperforms the existing works.
  arXiv  Detail & Related papers  (2021-09-13T10:07:26Z)
• Neural Methods for Effective, Efficient, and Exposure-Aware Information Retrieval [7.3371176873092585]
  We present novel neural architectures and methods motivated by the specific needs and challenges of information retrieval. In many real-life IR tasks, the retrieval involves extremely large collections--such as the document index of a commercial Web search engine--containing billions of documents.
  arXiv  Detail & Related papers  (2020-12-21T21:20:16Z)
• Learning from Context or Names? An Empirical Study on Neural Relation Extraction [112.06614505580501]
  We study the effect of two main information sources in text: textual context and entity mentions (names) We propose an entity-masked contrastive pre-training framework for relation extraction (RE) Our framework can improve the effectiveness and robustness of neural models in different RE scenarios.
  arXiv  Detail & Related papers  (2020-10-05T11:21:59Z)
• Abstractive Summarization for Low Resource Data using Domain Transfer and Data Synthesis [1.148539813252112]
  We explore using domain transfer and data synthesis to improve the performance of recent abstractive summarization methods. We show that tuning state of the art model trained on newspaper data could boost performance on student reflection data. We propose a template-based model to synthesize new data, which when incorporated into training further increased ROUGE scores.
  arXiv  Detail & Related papers  (2020-02-09T17:49:08Z)

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.