Neural text matching models have been used in a range of applications such as
question answering and natural language inference, and have yielded a good
performance. However, these neural models are of a limited adaptability,
resulting in a decline in performance when encountering test examples from a
different dataset or even a different task. The adaptability is particularly
important in the few-shot setting: in many cases, there is only a limited
amount of labeled data available for a target dataset or task, while we may
have access to a richly labeled source dataset or task. However, adapting a
model trained on the abundant source data to a few-shot target dataset or task
is challenging. To tackle this challenge, we propose a Meta-Weight Regulator
(MWR), which is a meta-learning approach that learns to assign weights to the
source examples based on their relevance to the target loss. Specifically, MWR
first trains the model on the uniformly weighted source examples, and measures
the efficacy of the model on the target examples via a loss function. By
iteratively performing a (meta) gradient descent, high-order gradients are
propagated to the source examples. These gradients are then used to update the
weights of source examples, in a way that is relevant to the target
performance. As MWR is model-agnostic, it can be applied to any backbone neural
model. Extensive experiments are conducted with various backbone text matching
models, on four widely used datasets and two tasks. The results demonstrate
that our proposed approach significantly outperforms a number of existing
adaptation methods and effectively improves the cross-dataset and cross-task
adaptability of the neural text matching models in the few-shot setting.

本文提出了一种元学习方法 Meta-Weight Regulator（MWR），可用于将源数据上训练过的神经文本匹配模型适应到少样本目标数据或任务上，以显著提高模型跨数据集和跨任务的适应能力。

元学习权重调节器实现可适应的文本匹配

Adaptable Text Matching via Meta-Weight Regulator

Neural text matching models have been widely used in community question
answering, information retrieval, and dialogue. However, these models designed
for short texts cannot well address the long-form text matching problem,
because there are many contexts in long-form texts can not be directly aligned
with each other, and it is difficult for existing models to capture the key
matching signals from such noisy data. Besides, these models are
computationally expensive for simply use all textual data indiscriminately. To
tackle the effectiveness and efficiency problem, we propose a novel
hierarchical noise filtering model, namely Match-Ignition. The main idea is to
plug the well-known PageRank algorithm into the Transformer, to identify and
filter both sentence and word level noisy information in the matching process.
Noisy sentences are usually easy to detect because previous work has shown that
their similarity can be explicitly evaluated by the word overlapping, so we
directly use PageRank to filter such information based on a sentence similarity
graph. Unlike sentences, words rely on their contexts to express concrete
meanings, so we propose to jointly learn the filtering and matching process, to
well capture the critical word-level matching signals. Specifically, a word
graph is first built based on the attention scores in each self-attention block
of Transformer, and key words are then selected by applying PageRank on this
graph. In this way, noisy words will be filtered out layer by layer in the
matching process. Experimental results show that Match-Ignition outperforms
both SOTA short text matching models and recent long-form text matching models.
We also conduct detailed analysis to show that Match-Ignition efficiently
captures important sentences and words, to facilitate the long-form text
matching process.

提出一种层次噪声过滤模型 Match-Ignition，通过将 PageRank 算法嵌入 Transformer 并在匹配过程中在句子和单词级别识别和过滤噪声信息，以提高长型文本匹配问题的有效性和效率。

Match-Ignition: 使用 PageRank 插入 Transformer，用于长篇文本匹配

Match-Ignition: Plugging PageRank into Transformer for Long-form Text  Matching

Building multi-turn information-seeking conversation systems is an important
and challenging research topic. Although several advanced neural text matching
models have been proposed for this task, they are generally not efficient for
industrial applications. Furthermore, they rely on a large amount of labeled
data, which may not be available in real-world applications. To alleviate these
problems, we study transfer learning for multi-turn information seeking
conversations in this paper. We first propose an efficient and effective
multi-turn conversation model based on convolutional neural networks. After
that, we extend our model to adapt the knowledge learned from a resource-rich
domain to enhance the performance. Finally, we deployed our model in an
industrial chatbot called AliMe Assist
(this https URL) and observed a significant
improvement over the existing online model.

本研究旨在解决多轮信息检索对话系统中先前存在的困难与不足，通过提出一种基于卷积神经网络的高效且有效的多轮会话模型，进而研究转移学习以在资源贫少的领域中提高性能，最终将模型应用于阿里巴巴的工业聊天机器人 AliMe Assist 中，取得了显著的性能提升。