19,043 research outputs found
Target-Guided Open-Domain Conversation
Many real-world open-domain conversation applications have specific goals to
achieve during open-ended chats, such as recommendation, psychotherapy,
education, etc. We study the problem of imposing conversational goals on
open-domain chat agents. In particular, we want a conversational system to chat
naturally with human and proactively guide the conversation to a designated
target subject. The problem is challenging as no public data is available for
learning such a target-guided strategy. We propose a structured approach that
introduces coarse-grained keywords to control the intended content of system
responses. We then attain smooth conversation transition through turn-level
supervised learning, and drive the conversation towards the target with
discourse-level constraints. We further derive a keyword-augmented conversation
dataset for the study. Quantitative and human evaluations show our system can
produce meaningful and effective conversations, significantly improving over
other approaches.Comment: ACL 2019. Data and code available at
https://github.com/squareRoot3/Target-Guided-Conversation. fixed typo
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
Recent advances in conversational NLP : Towards the standardization of Chatbot building
Dialogue systems have become recently essential in our life. Their use is
getting more and more fluid and easy throughout the time. This boils down to
the improvements made in NLP and AI fields. In this paper, we try to provide an
overview to the current state of the art of dialogue systems, their categories
and the different approaches to build them. We end up with a discussion that
compares all the techniques and analyzes the strengths and weaknesses of each.
Finally, we present an opinion piece suggesting to orientate the research
towards the standardization of dialogue systems building.Comment: 8 pages with references, 1 figur
Improving Search through A3C Reinforcement Learning based Conversational Agent
We develop a reinforcement learning based search assistant which can assist
users through a set of actions and sequence of interactions to enable them
realize their intent. Our approach caters to subjective search where the user
is seeking digital assets such as images which is fundamentally different from
the tasks which have objective and limited search modalities. Labeled
conversational data is generally not available in such search tasks and
training the agent through human interactions can be time consuming. We propose
a stochastic virtual user which impersonates a real user and can be used to
sample user behavior efficiently to train the agent which accelerates the
bootstrapping of the agent. We develop A3C algorithm based context preserving
architecture which enables the agent to provide contextual assistance to the
user. We compare the A3C agent with Q-learning and evaluate its performance on
average rewards and state values it obtains with the virtual user in validation
episodes. Our experiments show that the agent learns to achieve higher rewards
and better states.Comment: 17 pages, 7 figure
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
Towards Coherent and Engaging Spoken Dialog Response Generation Using Automatic Conversation Evaluators
Encoder-decoder based neural architectures serve as the basis of
state-of-the-art approaches in end-to-end open domain dialog systems. Since
most of such systems are trained with a maximum likelihood~(MLE) objective they
suffer from issues such as lack of generalizability and the generic response
problem, i.e., a system response that can be an answer to a large number of
user utterances, e.g., "Maybe, I don't know." Having explicit feedback on the
relevance and interestingness of a system response at each turn can be a useful
signal for mitigating such issues and improving system quality by selecting
responses from different approaches. Towards this goal, we present a system
that evaluates chatbot responses at each dialog turn for coherence and
engagement. Our system provides explicit turn-level dialog quality feedback,
which we show to be highly correlated with human evaluation. To show that
incorporating this feedback in the neural response generation models improves
dialog quality, we present two different and complementary mechanisms to
incorporate explicit feedback into a neural response generation model:
reranking and direct modification of the loss function during training. Our
studies show that a response generation model that incorporates these combined
feedback mechanisms produce more engaging and coherent responses in an
open-domain spoken dialog setting, significantly improving the response quality
using both automatic and human evaluation
A Tailored Pre-Training Model for Task-Oriented Dialog Generation
The recent success of large pre-trained language models such as BERT and
GPT-2 has suggested the effectiveness of incorporating language priors in
downstream dialog generation tasks. However, the performance of pre-trained
models on the dialog task is not as optimal as expected. In this paper, we
propose a Pre-trained Role Alternating Language model (PRAL), designed
specifically for task-oriented conversational systems. We adopted (Wu et al.,
2019) that models two speakers separately. We also design several techniques,
such as start position randomization, knowledge distillation, and history
discount to improve pre-training performance. We introduce a task-oriented
dialog pretraining dataset by cleaning 13 existing data sets. We test PRAL on
three different downstream tasks. The results show that PRAL performs better or
on par with state-of-the-art methods.Comment: 7 pages, 1 figur
Image Chat: Engaging Grounded Conversations
To achieve the long-term goal of machines being able to engage humans in
conversation, our models should captivate the interest of their speaking
partners. Communication grounded in images, whereby a dialogue is conducted
based on a given photo, is a setup naturally appealing to humans (Hu et al.,
2014). In this work we study large-scale architectures and datasets for this
goal. We test a set of neural architectures using state-of-the-art image and
text representations, considering various ways to fuse the components. To test
such models, we collect a dataset of grounded human-human conversations, where
speakers are asked to play roles given a provided emotional mood or style, as
the use of such traits is also a key factor in engagingness (Guo et al., 2019).
Our dataset, Image-Chat, consists of 202k dialogues over 202k images using 215
possible style traits. Automatic metrics and human evaluations of engagingness
show the efficacy of our approach; in particular, we obtain state-of-the-art
performance on the existing IGC task, and our best performing model is almost
on par with humans on the Image-Chat test set (preferred 47.7% of the time).Comment: ACL 202
Learning from Dialogue after Deployment: Feed Yourself, Chatbot!
The majority of conversations a dialogue agent sees over its lifetime occur
after it has already been trained and deployed, leaving a vast store of
potential training signal untapped. In this work, we propose the self-feeding
chatbot, a dialogue agent with the ability to extract new training examples
from the conversations it participates in. As our agent engages in
conversation, it also estimates user satisfaction in its responses. When the
conversation appears to be going well, the user's responses become new training
examples to imitate. When the agent believes it has made a mistake, it asks for
feedback; learning to predict the feedback that will be given improves the
chatbot's dialogue abilities further. On the PersonaChat chit-chat dataset with
over 131k training examples, we find that learning from dialogue with a
self-feeding chatbot significantly improves performance, regardless of the
amount of traditional supervision.Comment: ACL 201
The Dialogue Dodecathlon: Open-Domain Knowledge and Image Grounded Conversational Agents
We introduce dodecaDialogue: a set of 12 tasks that measures if a
conversational agent can communicate engagingly with personality and empathy,
ask questions, answer questions by utilizing knowledge resources, discuss
topics and situations, and perceive and converse about images. By multi-tasking
on such a broad large-scale set of data, we hope to both move towards and
measure progress in producing a single unified agent that can perceive, reason
and converse with humans in an open-domain setting. We show that such
multi-tasking improves over a BERT pre-trained baseline, largely due to
multi-tasking with very large dialogue datasets in a similar domain, and that
the multi-tasking in general provides gains to both text and image-based tasks
using several metrics in both the fine-tune and task transfer settings. We
obtain state-of-the-art results on many of the tasks, providing a strong
baseline for this challenge.Comment: ACL 202
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