23 research outputs found
Dialogue-Based Relation Extraction
We present the first human-annotated dialogue-based relation extraction (RE)
dataset DialogRE, aiming to support the prediction of relation(s) between two
arguments that appear in a dialogue. We further offer DialogRE as a platform
for studying cross-sentence RE as most facts span multiple sentences. We argue
that speaker-related information plays a critical role in the proposed task,
based on an analysis of similarities and differences between dialogue-based and
traditional RE tasks. Considering the timeliness of communication in a
dialogue, we design a new metric to evaluate the performance of RE methods in a
conversational setting and investigate the performance of several
representative RE methods on DialogRE. Experimental results demonstrate that a
speaker-aware extension on the best-performing model leads to gains in both the
standard and conversational evaluation settings. DialogRE is available at
https://dataset.org/dialogre/.Comment: To appear in ACL 202
TREND: Trigger-Enhanced Relation-Extraction Network for Dialogues
The goal of dialogue relation extraction (DRE) is to identify the relation
between two entities in a given dialogue. During conversations, speakers may
expose their relations to certain entities by explicit or implicit clues, such
evidences called "triggers". However, trigger annotations may not be always
available for the target data, so it is challenging to leverage such
information for enhancing the performance. Therefore, this paper proposes to
learn how to identify triggers from the data with trigger annotations and then
transfers the trigger-finding capability to other datasets for better
performance. The experiments show that the proposed approach is capable of
improving relation extraction performance of unseen relations and also
demonstrate the transferability of our proposed trigger-finding model across
different domains and datasets.Comment: Accepted to SIGDIAL 2022; The first two authors contributed to this
work equall
Disentangled Contrastive Learning for Learning Robust Textual Representations
Although the self-supervised pre-training of transformer models has resulted
in the revolutionizing of natural language processing (NLP) applications and
the achievement of state-of-the-art results with regard to various benchmarks,
this process is still vulnerable to small and imperceptible permutations
originating from legitimate inputs. Intuitively, the representations should be
similar in the feature space with subtle input permutations, while large
variations occur with different meanings. This motivates us to investigate the
learning of robust textual representation in a contrastive manner. However, it
is non-trivial to obtain opposing semantic instances for textual samples. In
this study, we propose a disentangled contrastive learning method that
separately optimizes the uniformity and alignment of representations without
negative sampling. Specifically, we introduce the concept of momentum
representation consistency to align features and leverage power normalization
while conforming the uniformity. Our experimental results for the NLP
benchmarks demonstrate that our approach can obtain better results compared
with the baselines, as well as achieve promising improvements with invariance
tests and adversarial attacks. The code is available in
https://github.com/zjunlp/DCL.Comment: Work in progres
DialogRE^C+: An Extension of DialogRE to Investigate How Much Coreference Helps Relation Extraction in Dialogs
Dialogue relation extraction (DRE) that identifies the relations between
argument pairs in dialogue text, suffers much from the frequent occurrence of
personal pronouns, or entity and speaker coreference. This work introduces a
new benchmark dataset DialogRE^C+, introducing coreference resolution into the
DRE scenario. With the aid of high-quality coreference knowledge, the reasoning
of argument relations is expected to be enhanced. In DialogRE^C+ dataset, we
manually annotate total 5,068 coreference chains over 36,369 argument mentions
based on the existing DialogRE data, where four different coreference chain
types namely speaker chain, person chain, location chain and organization chain
are explicitly marked. We further develop 4 coreference-enhanced graph-based
DRE models, which learn effective coreference representations for improving the
DRE task. We also train a coreference resolution model based on our annotations
and evaluate the effect of automatically extracted coreference chains
demonstrating the practicality of our dataset and its potential to other
domains and tasks.Comment: Accepted by NLPCC 202
Report on the Workshop on Personal Knowledge Graphs (PKG 2021) at AKBC 2021
The term personal knowledge graph (PKG) has been broadly used to refer to structured representation of information about a given user, primarily in the form of entities that are personally related to the user. The potential of personal knowledge graphs as a means of managing and organizing personal data, as well as a source of background knowledge for personalizing downstream services, has recently gained increasing attention from researchers in multiple fields, including that of Information Retrieval, Natural Language Processing, and the Semantic Web. The goal of the PKG’21 workshop was to create a forum for researchers and practitioners from diverse areas to present and discuss methods, tools, techniques, and experiences related to the construction and use of personal knowledge graphs, identify open questions, and create a shared research agenda. It successfully brought about a diverse workshop program, comprising an invited keynote, paper presentations, and breakout discussions, as a half-day event at the 3rd Automated Knowledge Base Construction (AKBC’21) conference. The workshop demonstrated that while the concept and research field of personal knowledge graphs is still in its early stages, there are many promising avenues of future development and research that already, and independently, have attracted the interest of several different communities.publishedVersio
ValueNet: A New Dataset for Human Value Driven Dialogue System
Building a socially intelligent agent involves many challenges, one of which
is to teach the agent to speak guided by its value like a human. However,
value-driven chatbots are still understudied in the area of dialogue systems.
Most existing datasets focus on commonsense reasoning or social norm modeling.
In this work, we present a new large-scale human value dataset called ValueNet,
which contains human attitudes on 21,374 text scenarios. The dataset is
organized in ten dimensions that conform to the basic human value theory in
intercultural research. We further develop a Transformer-based value regression
model on ValueNet to learn the utility distribution. Comprehensive empirical
results show that the learned value model could benefit a wide range of
dialogue tasks. For example, by teaching a generative agent with reinforcement
learning and the rewards from the value model, our method attains
state-of-the-art performance on the personalized dialog generation dataset:
Persona-Chat. With values as additional features, existing emotion recognition
models enable capturing rich human emotions in the context, which further
improves the empathetic response generation performance in the
EmpatheticDialogues dataset. To the best of our knowledge, ValueNet is the
first large-scale text dataset for human value modeling, and we are the first
one trying to incorporate a value model into emotionally intelligent dialogue
systems. The dataset is available at https://liang-qiu.github.io/ValueNet/.Comment: Paper accepted by AAAI 202