QuoteR: A Benchmark of Quote Recommendation for Writing
- URL: http://arxiv.org/abs/2202.13145v1
- Date: Sat, 26 Feb 2022 14:01:44 GMT
- Title: QuoteR: A Benchmark of Quote Recommendation for Writing
- Authors: Fanchao Qi, Yanhui Yang, Jing Yi, Zhili Cheng, Zhiyuan Liu, Maosong
Sun
- Abstract summary: We build a large and fully open quote recommendation dataset called QuoteR.
We conduct an extensive evaluation of existing quote recommendation methods on QuoteR.
We propose a new quote recommendation model that significantly outperforms previous methods on all three parts of QuoteR.
- Score: 80.83859760380616
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: It is very common to use quotations (quotes) to make our writings more
elegant or convincing. To help people find appropriate quotes more efficiently,
the task of quote recommendation is presented, aiming to recommend quotes that
fit the current context of writing. There have been various quote
recommendation approaches, but they are evaluated on different unpublished
datasets. To facilitate the research on this task, we build a large and fully
open quote recommendation dataset called QuoteR, which comprises three parts
including English, standard Chinese and classical Chinese. Any part of it is
larger than previous unpublished counterparts. We conduct an extensive
evaluation of existing quote recommendation methods on QuoteR. Furthermore, we
propose a new quote recommendation model that significantly outperforms
previous methods on all three parts of QuoteR. All the code and data of this
paper are available at https://github.com/thunlp/QuoteR.
Related papers
- QUILL: Quotation Generation Enhancement of Large Language Models [45.385109352200196]
Large language models (LLMs) have become excellent writing assistants, but struggle with quotation generation.
This is because they either hallucinate when providing factual quotations or fail to provide quotes that exceed human expectations.
We first establish a holistic and automatic evaluation system for quotation generation task, which consists of five criteria each with corresponding automatic metric.
We then construct a bilingual knowledge base that is broad in scope and rich in dimensions, containing up to 32,022 quotes.
arXiv Detail & Related papers (2024-11-06T05:24:09Z) - ILCiteR: Evidence-grounded Interpretable Local Citation Recommendation [31.259805200946175]
We introduce the evidence-grounded local citation recommendation task, where the target latent space comprises evidence spans for recommending specific papers.
Unlike past formulations that simply output recommendations, ILCiteR retrieves ranked lists of evidence span and recommended paper pairs.
We contribute a novel dataset for the evidence-grounded local citation recommendation task and demonstrate the efficacy of our proposed conditional neural rank-ensembling approach for re-ranking evidence spans.
arXiv Detail & Related papers (2024-03-13T17:38:05Z) - Impression-Aware Recommender Systems [57.38537491535016]
Novel data sources bring new opportunities to improve the quality of recommender systems.
Researchers may use impressions to refine user preferences and overcome the current limitations in recommender systems research.
We present a systematic literature review on recommender systems using impressions.
arXiv Detail & Related papers (2023-08-15T16:16:02Z) - Recommendation as Instruction Following: A Large Language Model
Empowered Recommendation Approach [83.62750225073341]
We consider recommendation as instruction following by large language models (LLMs)
We first design a general instruction format for describing the preference, intention, task form and context of a user in natural language.
Then we manually design 39 instruction templates and automatically generate a large amount of user-personalized instruction data.
arXiv Detail & Related papers (2023-05-11T17:39:07Z) - Tag-Aware Document Representation for Research Paper Recommendation [68.8204255655161]
We propose a hybrid approach that leverages deep semantic representation of research papers based on social tags assigned by users.
The proposed model is effective in recommending research papers even when the rating data is very sparse.
arXiv Detail & Related papers (2022-09-08T09:13:07Z) - Recommending Multiple Positive Citations for Manuscript via
Content-Dependent Modeling and Multi-Positive Triplet [6.7854900381386845]
We propose a novel scientific paper modeling for citation recommendations, namely Multi-Positive BERT Model for Citation Recommendation (MP-BERT4CR)
The proposed approach has the following advantages: First, the proposed multi-positive objectives are effective to recommend multiple positive candidates.
MP-BERT4CR are also effective in retrieving the full list of co-citations, and historically low-frequent co-citation pairs compared with the prior works.
arXiv Detail & Related papers (2021-11-25T04:09:31Z) - Learning Neural Textual Representations for Citation Recommendation [7.227232362460348]
We propose a novel approach to citation recommendation using a deep sequential representation of the documents (Sentence-BERT) cascaded with Siamese and triplet networks in a submodular scoring function.
To the best of our knowledge, this is the first approach to combine deep representations and submodular selection for a task of citation recommendation.
arXiv Detail & Related papers (2020-07-08T12:38:50Z) - Context-Based Quotation Recommendation [60.93257124507105]
We propose a novel context-aware quote recommendation system.
It generates a ranked list of quotable paragraphs and spans of tokens from a given source document.
We conduct experiments on a collection of speech transcripts and associated news articles.
arXiv Detail & Related papers (2020-05-17T17:49:53Z) - Citation Recommendation: Approaches and Datasets [20.47628019708079]
Citation recommendation describes the task of recommending citations for a given text.
In recent years, several approaches and evaluation data sets have been presented.
No literature survey has been conducted explicitly on citation recommendation.
arXiv Detail & Related papers (2020-02-17T13:59:50Z)
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.