Personalized chatbots focus on endowing chatbots with a consistent
personality to behave like real users, give more informative responses, and
further act as personal assistants. Existing personalized approaches tried to
incorporate several text descriptions as explicit user profiles. However, the
acquisition of such explicit profiles is expensive and time-consuming, thus
being impractical for large-scale real-world applications. Moreover, the
restricted predefined profile neglects the language behavior of a real user and
cannot be automatically updated together with the change of user interests. In
this paper, we propose to learn implicit user profiles automatically from
large-scale user dialogue history for building personalized chatbots.
Specifically, leveraging the benefits of Transformer on language understanding,
we train a personalized language model to construct a general user profile from
the user's historical responses. To highlight the relevant historical responses
to the input post, we further establish a key-value memory network of
historical post-response pairs, and build a dynamic post-aware user profile.
The dynamic profile mainly describes what and how the user has responded to
similar posts in history. To explicitly utilize users' frequently used words,
we design a personalized decoder to fuse two decoding strategies, including
generating a word from the generic vocabulary and copying one word from the
user's personalized vocabulary. Experiments on two real-world datasets show the
significant improvement of our model compared with existing methods. Our code
is available at this https URL

本文提出了一种从用户对话历史中自动学习隐式用户配置文件以构建个性化聊天机器人的方法，采用 Transformer 进行语言理解训练，并通过建立历史帖子 - 响应对的键值存储网络来建立动态的贴子感知用户配置文件。同时，采用个性化解码器融合两种解码策略，包括生成通用词汇表中的单词和从用户个性化词汇表中复制一个单词。实验结果表明，该模型较现有方法具有显著的改进。

针对隐式用户属性创建个性化聊天机器人方案：每人一机器人

One Chatbot Per Person: Creating Personalized Chatbots based on Implicit  User Profiles

Named entity recognition (NER) models are typically based on the architecture
of Bi-directional LSTM (BiLSTM). The constraints of sequential nature and the
modeling of single input prevent the full utilization of global information
from larger scope, not only in the entire sentence, but also in the entire
document (dataset). In this paper, we address these two deficiencies and
propose a model augmented with hierarchical contextualized representation:
sentence-level representation and document-level representation. In
sentence-level, we take different contributions of words in a single sentence
into consideration to enhance the sentence representation learned from an
independent BiLSTM via label embedding attention mechanism. In document-level,
the key-value memory network is adopted to record the document-aware
information for each unique word which is sensitive to similarity of context
information. Our two-level hierarchical contextualized representations are
fused with each input token embedding and corresponding hidden state of BiLSTM,
respectively. The experimental results on three benchmark NER datasets
(CoNLL-2003 and Ontonotes 5.0 English datasets, CoNLL-2002 Spanish dataset)
show that we establish new state-of-the-art results.

本文提出了一种基于分层上下文表示的模型，通过句子级和文档级表示来提高实体识别模型中全局信息的利用，其中包括标签嵌入和注意机制以及键值内存网络，实验证明该模型在三个基准数据集上表现出优越的效果。