153,565 research outputs found
Incorporating Relevant Knowledge in Context Modeling and Response Generation
To sustain engaging conversation, it is critical for chatbots to make good
use of relevant knowledge. Equipped with a knowledge base, chatbots are able to
extract conversation-related attributes and entities to facilitate context
modeling and response generation. In this work, we distinguish the uses of
attribute and entity and incorporate them into the encoder-decoder architecture
in different manners. Based on the augmented architecture, our chatbot, namely
Mike, is able to generate responses by referring to proper entities from the
collected knowledge. To validate the proposed approach, we build a movie
conversation corpus on which the proposed approach significantly outperforms
other four knowledge-grounded models
Augmenting End-to-End Dialog Systems with Commonsense Knowledge
Building dialog agents that can converse naturally with humans is a
challenging yet intriguing problem of artificial intelligence. In open-domain
human-computer conversation, where the conversational agent is expected to
respond to human responses in an interesting and engaging way, commonsense
knowledge has to be integrated into the model effectively. In this paper, we
investigate the impact of providing commonsense knowledge about the concepts
covered in the dialog. Our model represents the first attempt to integrating a
large commonsense knowledge base into end-to-end conversational models. In the
retrieval-based scenario, we propose the Tri-LSTM model to jointly take into
account message and commonsense for selecting an appropriate response. Our
experiments suggest that the knowledge-augmented models are superior to their
knowledge-free counterparts in automatic evaluation
A Survey of Document Grounded Dialogue Systems (DGDS)
Dialogue system (DS) attracts great attention from industry and academia
because of its wide application prospects. Researchers usually divide the DS
according to the function. However, many conversations require the DS to switch
between different functions. For example, movie discussion can change from
chit-chat to QA, the conversational recommendation can transform from chit-chat
to recommendation, etc. Therefore, classification according to functions may
not be enough to help us appreciate the current development trend. We classify
the DS based on background knowledge. Specifically, study the latest DS based
on the unstructured document(s). We define Document Grounded Dialogue System
(DGDS) as the DS that the dialogues are centering on the given document(s). The
DGDS can be used in scenarios such as talking over merchandise against product
Manual, commenting on news reports, etc. We believe that extracting
unstructured document(s) information is the future trend of the DS because a
great amount of human knowledge lies in these document(s). The research of the
DGDS not only possesses a broad application prospect but also facilitates AI to
better understand human knowledge and natural language. We analyze the
classification, architecture, datasets, models, and future development trends
of the DGDS, hoping to help researchers in this field.Comment: 30 pages, 4 figures, 13 table
Incorporating Loose-Structured Knowledge into Conversation Modeling via Recall-Gate LSTM
Modeling human conversations is the essence for building satisfying chat-bots
with multi-turn dialog ability. Conversation modeling will notably benefit from
domain knowledge since the relationships between sentences can be clarified due
to semantic hints introduced by knowledge. In this paper, a deep neural network
is proposed to incorporate background knowledge for conversation modeling.
Through a specially designed Recall gate, domain knowledge can be transformed
into the extra global memory of Long Short-Term Memory (LSTM), so as to enhance
LSTM by cooperating with its local memory to capture the implicit semantic
relevance between sentences within conversations. In addition, this paper
introduces the loose structured domain knowledge base, which can be built with
slight amount of manual work and easily adopted by the Recall gate. Our model
is evaluated on the context-oriented response selecting task, and experimental
results on both two datasets have shown that our approach is promising for
modeling human conversations and building key components of automatic chatting
systems.Comment: under review of IJCNN 2017; 10 pages, 5 figure
Extending Neural Generative Conversational Model using External Knowledge Sources
The use of connectionist approaches in conversational agents has been
progressing rapidly due to the availability of large corpora. However current
generative dialogue models often lack coherence and are content poor. This work
proposes an architecture to incorporate unstructured knowledge sources to
enhance the next utterance prediction in chit-chat type of generative dialogue
models. We focus on Sequence-to-Sequence (Seq2Seq) conversational agents
trained with the Reddit News dataset, and consider incorporating external
knowledge from Wikipedia summaries as well as from the NELL knowledge base. Our
experiments show faster training time and improved perplexity when leveraging
external knowledge.Comment: Accepted in EMNLP 201
Improving Response Selection in Multi-Turn Dialogue Systems by Incorporating Domain Knowledge
Building systems that can communicate with humans is a core problem in
Artificial Intelligence. This work proposes a novel neural network architecture
for response selection in an end-to-end multi-turn conversational dialogue
setting. The architecture applies context level attention and incorporates
additional external knowledge provided by descriptions of domain-specific
words. It uses a bi-directional Gated Recurrent Unit (GRU) for encoding context
and responses and learns to attend over the context words given the latent
response representation and vice versa.In addition, it incorporates external
domain specific information using another GRU for encoding the domain keyword
descriptions. This allows better representation of domain-specific keywords in
responses and hence improves the overall performance. Experimental results show
that our model outperforms all other state-of-the-art methods for response
selection in multi-turn conversations.Comment: Published as conference paper at CoNLL 201
Review-Driven Answer Generation for Product-Related Questions in E-Commerce
The users often have many product-related questions before they make a
purchase decision in E-commerce. However, it is often time-consuming to examine
each user review to identify the desired information. In this paper, we propose
a novel review-driven framework for answer generation for product-related
questions in E-commerce, named RAGE. We develope RAGE on the basis of the
multi-layer convolutional architecture to facilitate speed-up of answer
generation with the parallel computation. For each question, RAGE first
extracts the relevant review snippets from the reviews of the corresponding
product. Then, we devise a mechanism to identify the relevant information from
the noise-prone review snippets and incorporate this information to guide the
answer generation. The experiments on two real-world E-Commerce datasets show
that the proposed RAGE significantly outperforms the existing alternatives in
producing more accurate and informative answers in natural language. Moreover,
RAGE takes much less time for both model training and answer generation than
the existing RNN based generation models
Contextual Topic Modeling For Dialog Systems
Accurate prediction of conversation topics can be a valuable signal for
creating coherent and engaging dialog systems. In this work, we focus on
context-aware topic classification methods for identifying topics in free-form
human-chatbot dialogs. We extend previous work on neural topic classification
and unsupervised topic keyword detection by incorporating conversational
context and dialog act features. On annotated data, we show that incorporating
context and dialog acts leads to relative gains in topic classification
accuracy by 35% and on unsupervised keyword detection recall by 11% for
conversational interactions where topics frequently span multiple utterances.
We show that topical metrics such as topical depth is highly correlated with
dialog evaluation metrics such as coherence and engagement implying that
conversational topic models can predict user satisfaction. Our work for
detecting conversation topics and keywords can be used to guide chatbots
towards coherent dialog
Global-to-local Memory Pointer Networks for Task-Oriented Dialogue
End-to-end task-oriented dialogue is challenging since knowledge bases are
usually large, dynamic and hard to incorporate into a learning framework. We
propose the global-to-local memory pointer (GLMP) networks to address this
issue. In our model, a global memory encoder and a local memory decoder are
proposed to share external knowledge. The encoder encodes dialogue history,
modifies global contextual representation, and generates a global memory
pointer. The decoder first generates a sketch response with unfilled slots.
Next, it passes the global memory pointer to filter the external knowledge for
relevant information, then instantiates the slots via the local memory
pointers. We empirically show that our model can improve copy accuracy and
mitigate the common out-of-vocabulary problem. As a result, GLMP is able to
improve over the previous state-of-the-art models in both simulated bAbI
Dialogue dataset and human-human Stanford Multi-domain Dialogue dataset on
automatic and human evaluation.Comment: ICLR 201
The Design and Implementation of XiaoIce, an Empathetic Social Chatbot
This paper describes the development of Microsoft XiaoIce, the most popular
social chatbot in the world. XiaoIce is uniquely designed as an AI companion
with an emotional connection to satisfy the human need for communication,
affection, and social belonging. We take into account both intelligent quotient
(IQ) and emotional quotient (EQ) in system design, cast human-machine social
chat as decision-making over Markov Decision Processes (MDPs), and optimize
XiaoIce for long-term user engagement, measured in expected Conversation-turns
Per Session (CPS). We detail the system architecture and key components
including dialogue manager, core chat, skills, and an empathetic computing
module. We show how XiaoIce dynamically recognizes human feelings and states,
understands user intent, and responds to user needs throughout long
conversations. Since her launch in 2014, XiaoIce has communicated with over 660
million active users and succeeded in establishing long-term relationships with
many of them. Analysis of large scale online logs shows that XiaoIce has
achieved an average CPS of 23, which is significantly higher than that of other
chatbots and even human conversations
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