The dialogue-based relation extraction (DialogRE) task aims to predict the
relations between argument pairs that appear in dialogue. Most previous studies
utilize fine-tuning pre-trained language models (PLMs) only with extensive
features to supplement the low information density of the dialogue by multiple
speakers. To effectively exploit inherent knowledge of PLMs without extra
layers and consider scattered semantic cues on the relation between the
arguments, we propose a Guiding model with RelAtional Semantics using Prompt
(GRASP). We adopt a prompt-based fine-tuning approach and capture relational
semantic clues of a given dialogue with 1) an argument-aware prompt marker
strategy and 2) the relational clue detection task. In the experiments, GRASP
achieves state-of-the-art performance in terms of both F1 and F1c scores on a
DialogRE dataset even though our method only leverages PLMs without adding any
extra layers.

本文提出了一个利用 prompt-based fine-tuning 的 Guiding model with RelAtional Semantics (GRASP) 方法，在不添加额外层次的情况下，通过一种关注 argument 的 prompt 标记策略和关系线索检测任务，从语料库中提取出相关语义线索，并在 DialogRE 数据集上实现了 F1 和 F1c 得分的最先进性能。

使用提示引导具有关系语义的模型进行对话关系抽取

GRASP: Guiding model with RelAtional Semantics using Prompt for Dialogue Relation Extraction

The state-of-the-art solutions to the vocabulary mismatch in information
retrieval (IR) mainly aim at leveraging either the relational semantics
provided by external resources or the distributional semantics, recently
investigated by deep neural approaches. Guided by the intuition that the
relational semantics might improve the effectiveness of deep neural approaches,
we propose the Deep Semantic Resource Inference Model (DSRIM) that relies on:
1) a representation of raw-data that models the relational semantics of text by
jointly considering objects and relations expressed in a knowledge resource,
and 2) an end-to-end neural architecture that learns the query-document
relevance by leveraging the distributional and relational semantics of
documents and queries. The experimental evaluation carried out on two TREC
datasets from TREC Terabyte and TREC CDS tracks relying respectively on WordNet
and MeSH resources, indicates that our model outperforms state-of-the-art
semantic and deep neural IR models.

本文介绍了一种深度语义资源推理模型，实现了在信息检索中解决词汇不匹配问题，并在 TREC Terabyte 和 TREC CDS 数据集上展示了其在语义和深度学习 IR 模型方面的优越性。

DSRIM：一种由知识资源驱动的文档表示增强的深度神经信息检索模型

DSRIM: A Deep Neural Information Retrieval Model Enhanced by a Knowledge  Resource Driven Representation of Documents

We investigate the hypothesis that word representations ought to incorporate
both distributional and relational semantics. To this end, we employ the
Alternating Direction Method of Multipliers (ADMM), which flexibly optimizes a
distributional objective on raw text and a relational objective on WordNet.
Preliminary results on knowledge base completion, analogy tests, and parsing
show that word representations trained on both objectives can give improvements
in some cases.

本文研究了词表示应该包含分布式和关系语义这一假设。为此，我们使用 Alternating Direction Method of Multipliers (ADMM)，它可以灵活地在原始文本上优化分布式目标和 WordNet 上的关系目标。初步结果表明，在一些情况下，在两种目标上训练的词表示可以改进知识库完成、类比测试和解析等方面。

在词汇表示中结合分布和关系语义

Incorporating Both Distributional and Relational Semantics in Word  Representations

本文研究单词表示应该结合分布式和关联语义的假说。我们采用 ADMM 方法，在原始文本上灵活优化分布式目标，WordNet 上的关联目标。初步结果表明，针对两个目标训练的单词表示在某些情况下可以提高知识库完成，类比测试和解析的性能。