113 research outputs found
PrisCrawler: A Relevance Based Crawler for Automated Data Classification from Bulletin Board
Nowadays people realize that it is difficult to find information simply and
quickly on the bulletin boards. In order to solve this problem, people propose
the concept of bulletin board search engine. This paper describes the
priscrawler system, a subsystem of the bulletin board search engine, which can
automatically crawl and add the relevance to the classified attachments of the
bulletin board. Priscrawler utilizes Attachrank algorithm to generate the
relevance between webpages and attachments and then turns bulletin board into
clear classified and associated databases, making the search for attachments
greatly simplified. Moreover, it can effectively reduce the complexity of
pretreatment subsystem and retrieval subsystem and improve the search
precision. We provide experimental results to demonstrate the efficacy of the
priscrawler.Comment: published in GCIS of IEEE WRI '0
Semantic Parsing by Large Language Models for Intricate Updating Strategies of Zero-Shot Dialogue State Tracking
Zero-shot Dialogue State Tracking (DST) addresses the challenge of acquiring
and annotating task-oriented dialogues, which can be time-consuming and costly.
However, DST extends beyond simple slot-filling and requires effective updating
strategies for tracking dialogue state as conversations progress. In this
paper, we propose ParsingDST, a new In-Context Learning (ICL) method, to
introduce additional intricate updating strategies in zero-shot DST. Our
approach reformulates the DST task by leveraging powerful Large Language Models
(LLMs) and translating the original dialogue text to JSON through semantic
parsing as an intermediate state. We also design a novel framework that
includes more modules to ensure the effectiveness of updating strategies in the
text-to-JSON process. Experimental results demonstrate that our approach
outperforms existing zero-shot DST methods on MultiWOZ, exhibiting significant
improvements in Joint Goal Accuracy (JGA) and slot accuracy compared to
existing ICL methods. Our code has been released.Comment: Accepted to the Findings of EMNLP 2023 (Short Paper
Inverse Game Theory for Stackelberg Games: the Blessing of Bounded Rationality
Optimizing strategic decisions (a.k.a. computing equilibrium) is key to the
success of many non-cooperative multi-agent applications. However, in many
real-world situations, we may face the exact opposite of this game-theoretic
problem -- instead of prescribing equilibrium of a given game, we may directly
observe the agents' equilibrium behaviors but want to infer the underlying
parameters of an unknown game. This research question, also known as inverse
game theory, has been studied in multiple recent works in the context of
Stackelberg games. Unfortunately, existing works exhibit quite negative
results, showing statistical hardness and computational hardness, assuming
follower's perfectly rational behaviors. Our work relaxes the perfect
rationality agent assumption to the classic quantal response model, a more
realistic behavior model of bounded rationality. Interestingly, we show that
the smooth property brought by such bounded rationality model actually leads to
provably more efficient learning of the follower utility parameters in general
Stackelberg games. Systematic empirical experiments on synthesized games
confirm our theoretical results and further suggest its robustness beyond the
strict quantal response model
Providing Definitive Learning Direction for Relation Classification System
Deep neural network has adequately revealed its superiority of solving various tasks in Natural Language Processing, especially for relation classification. However, unlike traditional feature-engineering methods that targetedly extract well-designed features for specific task, the diversity of input format for deep learning is limited; word sequence as input is the frequently used setting. Therefore, the input of neural network, to some extent, lacks pertinence. For relation classification task, it is not uncommon that, without specific entity pair, a sentence contains various relation types; therefore, entity pair indicates the distribution of the crucial information in input sentence for recognizing specific relation. Aiming at this characteristic, in this paper, several strategies are proposed to integrate entity pair information into the application of deep learning in relation classification task, in a way to provide definitive learning direction for neural network. Experimental results on the SemEval-2010 Task 8 dataset show that our method outperforms most of the state-of-the-art models, without external linguistic features
Generative Adversarial Zero-Shot Relational Learning for Knowledge Graphs
Large-scale knowledge graphs (KGs) are shown to become more important in
current information systems. To expand the coverage of KGs, previous studies on
knowledge graph completion need to collect adequate training instances for
newly-added relations. In this paper, we consider a novel formulation,
zero-shot learning, to free this cumbersome curation. For newly-added
relations, we attempt to learn their semantic features from their text
descriptions and hence recognize the facts of unseen relations with no examples
being seen. For this purpose, we leverage Generative Adversarial Networks
(GANs) to establish the connection between text and knowledge graph domain: The
generator learns to generate the reasonable relation embeddings merely with
noisy text descriptions. Under this setting, zero-shot learning is naturally
converted to a traditional supervised classification task. Empirically, our
method is model-agnostic that could be potentially applied to any version of KG
embeddings, and consistently yields performance improvements on NELL and Wiki
dataset
FutureTOD: Teaching Future Knowledge to Pre-trained Language Model for Task-Oriented Dialogue
Pre-trained language models based on general text enable huge success in the
NLP scenario. But the intrinsical difference of linguistic patterns between
general text and task-oriented dialogues makes existing pre-trained language
models less useful in practice. Current dialogue pre-training methods rely on a
contrastive framework and face the challenges of both selecting true positives
and hard negatives. In this paper, we propose a novel dialogue pre-training
model, FutureTOD, which distills future knowledge to the representation of the
previous dialogue context using a self-training framework. Our intuition is
that a good dialogue representation both learns local context information and
predicts future information. Extensive experiments on diverse downstream
dialogue tasks demonstrate the effectiveness of our model, especially the
generalization, robustness, and learning discriminative dialogue
representations capabilities.Comment: ACL 2023 Main Conferenc
Seen to Unseen: Exploring Compositional Generalization of Multi-Attribute Controllable Dialogue Generation
Existing controllable dialogue generation work focuses on the
single-attribute control and lacks generalization capability to
out-of-distribution multiple attribute combinations. In this paper, we explore
the compositional generalization for multi-attribute controllable dialogue
generation where a model can learn from seen attribute values and generalize to
unseen combinations. We propose a prompt-based disentangled controllable
dialogue generation model, DCG. It learns attribute concept composition by
generating attribute-oriented prompt vectors and uses a disentanglement loss to
disentangle different attributes for better generalization. Besides, we design
a unified reference-free evaluation framework for multiple attributes with
different levels of granularities. Experiment results on two benchmarks prove
the effectiveness of our method and the evaluation metric.Comment: ACL 2023 Main Conferenc
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