70,767 research outputs found
Synthesising Contextually Appropriate Intonation In Limited Domains
We describe a method of synthesising contextually appropriate intonation with limited domain unit selection voices. The method enables the natural language generation component of a dialogue system to specify its intonation choices via APML, an XML-based markup language. In a pilot study, we built an APML-aware limited domain voice for use in flight information dialogues, and carried out a perception experiment comparing the APML voice to a default version built using the same recordings without the additional structure. The intonation produced by the APML voice was judged significantly more contextually appropriate than that of the default voice. These results justified building a second voice with a much larger vocabulary, using an automated script generation algorithm
Synthesising Contextually Appropriate Intonation in Limited Domains
We describe a method of synthesising contextually appropriate intonation with limited domain unit selection voices. The method enables the natural language generation component of a dialogue system to specify its intonation choices
via APML, an XML-based markup language. In a pilot study, we built an APML-aware limited domain voice for use in flight information dialogues, and carried out a perception experiment comparing the APML voice to a default version built using the same recordings without the additional structure. The intonation produced by the APML voice was judged significantly more contextually appropriate than that of the default voice. These results justified building a second voice with a much larger vocabulary, using an automated script generation algorithm
Production Ready Chatbots: Generate if not Retrieve
In this paper, we present a hybrid model that combines a neural
conversational model and a rule-based graph dialogue system that assists users
in scheduling reminders through a chat conversation. The graph based system has
high precision and provides a grammatically accurate response but has a low
recall. The neural conversation model can cater to a variety of requests, as it
generates the responses word by word as opposed to using canned responses. The
hybrid system shows significant improvements over the existing baseline system
of rule based approach and caters to complex queries with a domain-restricted
neural model. Restricting the conversation topic and combination of graph based
retrieval system with a neural generative model makes the final system robust
enough for a real world application.Comment: DEEPDIAL-18, AAAI-201
Neural Generative Question Answering
This paper presents an end-to-end neural network model, named Neural
Generative Question Answering (GENQA), that can generate answers to simple
factoid questions, based on the facts in a knowledge-base. More specifically,
the model is built on the encoder-decoder framework for sequence-to-sequence
learning, while equipped with the ability to enquire the knowledge-base, and is
trained on a corpus of question-answer pairs, with their associated triples in
the knowledge-base. Empirical study shows the proposed model can effectively
deal with the variations of questions and answers, and generate right and
natural answers by referring to the facts in the knowledge-base. The experiment
on question answering demonstrates that the proposed model can outperform an
embedding-based QA model as well as a neural dialogue model trained on the same
data.Comment: Accepted by IJCAI 201
The Natural Language Decathlon: Multitask Learning as Question Answering
Deep learning has improved performance on many natural language processing
(NLP) tasks individually. However, general NLP models cannot emerge within a
paradigm that focuses on the particularities of a single metric, dataset, and
task. We introduce the Natural Language Decathlon (decaNLP), a challenge that
spans ten tasks: question answering, machine translation, summarization,
natural language inference, sentiment analysis, semantic role labeling,
zero-shot relation extraction, goal-oriented dialogue, semantic parsing, and
commonsense pronoun resolution. We cast all tasks as question answering over a
context. Furthermore, we present a new Multitask Question Answering Network
(MQAN) jointly learns all tasks in decaNLP without any task-specific modules or
parameters in the multitask setting. MQAN shows improvements in transfer
learning for machine translation and named entity recognition, domain
adaptation for sentiment analysis and natural language inference, and zero-shot
capabilities for text classification. We demonstrate that the MQAN's
multi-pointer-generator decoder is key to this success and performance further
improves with an anti-curriculum training strategy. Though designed for
decaNLP, MQAN also achieves state of the art results on the WikiSQL semantic
parsing task in the single-task setting. We also release code for procuring and
processing data, training and evaluating models, and reproducing all
experiments for decaNLP
Generative Encoder-Decoder Models for Task-Oriented Spoken Dialog Systems with Chatting Capability
Generative encoder-decoder models offer great promise in developing
domain-general dialog systems. However, they have mainly been applied to
open-domain conversations. This paper presents a practical and novel framework
for building task-oriented dialog systems based on encoder-decoder models. This
framework enables encoder-decoder models to accomplish slot-value independent
decision-making and interact with external databases. Moreover, this paper
shows the flexibility of the proposed method by interleaving chatting
capability with a slot-filling system for better out-of-domain recovery. The
models were trained on both real-user data from a bus information system and
human-human chat data. Results show that the proposed framework achieves good
performance in both offline evaluation metrics and in task success rate with
human users.Comment: Accepted as a long paper in SIGIDIAL 201
Teacher-Student Framework Enhanced Multi-domain Dialogue Generation
Dialogue systems dealing with multi-domain tasks are highly required. How to
record the state remains a key problem in a task-oriented dialogue system.
Normally we use human-defined features as dialogue states and apply a state
tracker to extract these features. However, the performance of such a system is
limited by the error propagation of a state tracker. In this paper, we propose
a dialogue generation model that needs no external state trackers and still
benefits from human-labeled semantic data. By using a teacher-student
framework, several teacher models are firstly trained in their individual
domains, learn dialogue policies from labeled states. And then the learned
knowledge and experience are merged and transferred to a universal student
model, which takes raw utterance as its input. Experiments show that the
dialogue system trained under our framework outperforms the one uses a belief
tracker.Comment: Official Version: arXiv:2005.1045
Dynamic Fusion Network for Multi-Domain End-to-end Task-Oriented Dialog
Recent studies have shown remarkable success in end-to-end task-oriented
dialog system. However, most neural models rely on large training data, which
are only available for a certain number of task domains, such as navigation and
scheduling.
This makes it difficult to scalable for a new domain with limited labeled
data. However, there has been relatively little research on how to effectively
use data from all domains to improve the performance of each domain and also
unseen domains. To this end, we investigate methods that can make explicit use
of domain knowledge and introduce a shared-private network to learn shared and
specific knowledge. In addition, we propose a novel Dynamic Fusion Network
(DF-Net) which automatically exploit the relevance between the target domain
and each domain. Results show that our model outperforms existing methods on
multi-domain dialogue, giving the state-of-the-art in the literature. Besides,
with little training data, we show its transferability by outperforming prior
best model by 13.9\% on average.Comment: ACL202
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
Summarizing Decisions in Spoken Meetings
This paper addresses the problem of summarizing decisions in spoken meetings:
our goal is to produce a concise {\it decision abstract} for each meeting
decision. We explore and compare token-level and dialogue act-level automatic
summarization methods using both unsupervised and supervised learning
frameworks. In the supervised summarization setting, and given true clusterings
of decision-related utterances, we find that token-level summaries that employ
discourse context can approach an upper bound for decision abstracts derived
directly from dialogue acts. In the unsupervised summarization setting,we find
that summaries based on unsupervised partitioning of decision-related
utterances perform comparably to those based on partitions generated using
supervised techniques (0.22 ROUGE-F1 using LDA-based topic models vs. 0.23
using SVMs).Comment: ACL Workshop on Automatic Summarization for Different Genres, Media,
and Languages, 201
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