3 research outputs found
Generating Persuasive Responses to Customer Reviews with Multi-Source Prior Knowledge in E-commerce
Customer reviews usually contain much information about one's online shopping
experience. While positive reviews are beneficial to the stores, negative ones
will largely influence consumers' decision and may lead to a decline in sales.
Therefore, it is of vital importance to carefully and persuasively reply to
each negative review and minimize its disadvantageous effect. Recent studies
consider leveraging generation models to help the sellers respond. However,
this problem is not well-addressed as the reviews may contain multiple aspects
of issues which should be resolved accordingly and persuasively. In this work,
we propose a Multi-Source Multi-Aspect Attentive Generation model for
persuasive response generation. Various sources of information are
appropriately obtained and leveraged by the proposed model for generating more
informative and persuasive responses. A multi-aspect attentive network is
proposed to automatically attend to different aspects in a review and ensure
most of the issues are tackled. Extensive experiments on two real-world
datasets, demonstrate that our approach outperforms the state-of-the-art
methods and online tests prove that our deployed system significantly enhances
the efficiency of the stores' dealing with negative reviews.Comment: Accepted at CIKM 2022 applied researc
Rethinking Human-like Translation Strategy: Integrating Drift-Diffusion Model with Large Language Models for Machine Translation
Large language models (LLMs) have demonstrated promising potential in various
downstream tasks, including machine translation. However, prior work on
LLM-based machine translation has mainly focused on better utilizing training
data, demonstrations, or pre-defined and universal knowledge to improve
performance, with a lack of consideration of decision-making like human
translators. In this paper, we incorporate Thinker with the Drift-Diffusion
Model (Thinker-DDM) to address this issue. We then redefine the Drift-Diffusion
process to emulate human translators' dynamic decision-making under constrained
resources. We conduct extensive experiments under the high-resource,
low-resource, and commonsense translation settings using the WMT22 and CommonMT
datasets, in which Thinker-DDM outperforms baselines in the first two
scenarios. We also perform additional analysis and evaluation on commonsense
translation to illustrate the high effectiveness and efficacy of the proposed
method.Comment: Under revie
MGIMN: Multi-Grained Interactive Matching Network for Few-shot Text Classification
Text classification struggles to generalize to unseen classes with very few
labeled text instances per class. In such a few-shot learning (FSL) setting,
metric-based meta-learning approaches have shown promising results. Previous
studies mainly aim to derive a prototype representation for each class.
However, they neglect that it is challenging-yet-unnecessary to construct a
compact representation which expresses the entire meaning for each class. They
also ignore the importance to capture the inter-dependency between query and
the support set for few-shot text classification. To deal with these issues, we
propose a meta-learning based method MGIMN which performs instance-wise
comparison followed by aggregation to generate class-wise matching vectors
instead of prototype learning. The key of instance-wise comparison is the
interactive matching within the class-specific context and episode-specific
context. Extensive experiments demonstrate that the proposed method
significantly outperforms the existing state-of-the-art approaches, under both
the standard FSL and generalized FSL settings.Comment: 10 pages, 2 figures, 6 tabel