100 research outputs found
User Impressions of System Questions to Acquire Lexical Knowledge during Dialogues
We have been addressing the problem of acquiring attributes of unknown terms through dialogues and previously proposed an approach using the implicit confirmation process. It is crucial for dialogue systems to ask questions that do not diminish the user’s willingness to talk. In this paper, we conducted a user study to investigate user impression for several question types, including explicit and implicit, to acquire lexical knowledge. We clarified the order among the types and found that repeating the same question type annoys the user and degrades user impression even when the content of the questions is correct. We also propose a method for determining whether an estimated attribute is correct, which is included in an implicit question. The method exploits multiple-user responses to implicit questions about the attribute of the same unknown term. Experimental results revealed that the proposed method exhibited a higher precision rate for determining the correctly estimated attributes than when only single-user responses were considered
Joint semantic discourse models for automatic multi-document summarization
Automatic multi-document summarization aims at selecting the essential content of related documents and presenting it in a summary. In this paper, we propose some methods for automatic summarization based on Rhetorical Structure Theory and Cross-document Structure Theory. They are chosen in order to properly address the relevance of information, multidocument phenomena and subtopical distribution in the source texts. The results show that using semantic discourse knowledge in strategies for content selection produces summaries that are more informative.Sumarização automática multidocumento visa à seleção das informações mais importantes de um conjunto de documentos para produzir um sumário. Neste artigo, propõem-se métodos para sumarização automática baseando-se em conhecimento semântico-discursivo das teorias Rhetorical Structure Theory e Cross-document Structure Theory. Tais teorias foram escolhidas para tratar adequadamente a relevância das informações, os fenômenos multidocumento e a distribuição de subtópicos dos documentos. Os resultados mostram que o uso de conhecimento semântico-discursivo para selecionar conteúdo produz sumários mais informativos.FAPESPCAPE
A Personalized System for Conversational Recommendations
Searching for and making decisions about information is becoming increasingly
difficult as the amount of information and number of choices increases.
Recommendation systems help users find items of interest of a particular type,
such as movies or restaurants, but are still somewhat awkward to use. Our
solution is to take advantage of the complementary strengths of personalized
recommendation systems and dialogue systems, creating personalized aides. We
present a system -- the Adaptive Place Advisor -- that treats item selection as
an interactive, conversational process, with the program inquiring about item
attributes and the user responding. Individual, long-term user preferences are
unobtrusively obtained in the course of normal recommendation dialogues and
used to direct future conversations with the same user. We present a novel user
model that influences both item search and the questions asked during a
conversation. We demonstrate the effectiveness of our system in significantly
reducing the time and number of interactions required to find a satisfactory
item, as compared to a control group of users interacting with a non-adaptive
version of the system
UniPCM: Universal Pre-trained Conversation Model with Task-aware Automatic Prompt
Recent research has shown that multi-task pre-training greatly improves the
model's robustness and transfer ability, which is crucial for building a
high-quality dialog system. However, most previous works on multi-task
pre-training rely heavily on human-defined input format or prompt, which is not
optimal in quality and quantity. In this work, we propose to use Task-based
Automatic Prompt generation (TAP) to automatically generate high-quality
prompts. Using the high-quality prompts generated, we scale the corpus of the
pre-trained conversation model to 122 datasets from 15 dialog-related tasks,
resulting in Universal Pre-trained Conversation Model (UniPCM), a powerful
foundation model for various conversational tasks and different dialog systems.
Extensive experiments have shown that UniPCM is robust to input prompts and
capable of various dialog-related tasks. Moreover, UniPCM has strong transfer
ability and excels at low resource scenarios, achieving SOTA results on 9
different datasets ranging from task-oriented dialog to open-domain
conversation. Furthermore, we are amazed to find that TAP can generate prompts
on par with those collected with crowdsourcing. The code is released with the
paper
A Primer on Seq2Seq Models for Generative Chatbots
The recent spread of Deep Learning-based solutions for Artificial Intelligence and the development of Large Language Models has pushed forwards significantly the Natural Language Processing area. The approach has quickly evolved in the last ten years, deeply affecting NLP, from low-level text pre-processing tasks –such as tokenisation or POS tagging– to high-level, complex NLP applications like machine translation and chatbots. This paper examines recent trends in the development of open-domain data-driven generative chatbots, focusing on the Seq2Seq architectures. Such architectures are compatible with multiple learning approaches, ranging from supervised to reinforcement and, in the last years, allowed to realise very engaging open-domain chatbots. Not only do these architectures allow to directly output the next turn in a conversation but, to some extent, they also allow to control the style or content of the response. To offer a complete view on the subject, we examine possible architecture implementations as well as training and evaluation approaches. Additionally, we provide information about the openly available corpora to train and evaluate such models and about the current and past chatbot competitions. Finally, we present some insights on possible future directions, given the current research status
Data-efficient methods for dialogue systems
Conversational User Interface (CUI) has become ubiquitous in everyday life, in consumer-focused products like Siri and Alexa or more business-oriented customer support automation
solutions. Deep learning underlies many recent breakthroughs in dialogue systems but requires
very large amounts of training data, often annotated by experts — and this dramatically increases the cost of deploying such systems in production setups and reduces their flexibility as
software products. Trained with smaller data, these methods end up severely lacking robustness
to various phenomena of spoken language (e.g. disfluencies), out-of-domain input, and often
just have too little generalisation power to other tasks and domains.
In this thesis, we address the above issues by introducing a series of methods for bootstrapping
robust dialogue systems from minimal data. Firstly, we study two orthogonal approaches to dialogue: a linguistically informed model (DyLan) and a machine learning-based one (MemN2N) —
from the data efficiency perspective, i.e. their potential to generalise from minimal data and
robustness to natural spontaneous input. We outline the steps to obtain data-efficient solutions
with either approach and proceed with the neural models for the rest of the thesis.
We then introduce the core contributions of this thesis, two data-efficient models for dialogue
response generation: the Dialogue Knowledge Transfer Network (DiKTNet) based on transferable latent dialogue representations, and the Generative-Retrieval Transformer (GRTr) combining response generation logic with a retrieval mechanism as the fallback. GRTr ranked first at
the Dialog System Technology Challenge 8 Fast Domain Adaptation task.
Next, we the problem of training robust neural models from minimal data. As such, we look at
robustness to disfluencies and propose a multitask LSTM-based model for domain-general disfluency detection. We then go on to explore robustness to anomalous, or out-of-domain (OOD)
input. We address this problem by (1) presenting Turn Dropout, a data-augmentation technique
facilitating training for anomalous input only using in-domain data, and (2) introducing VHCN
and AE-HCN, autoencoder-augmented models for efficient training with turn dropout based on
the Hybrid Code Networks (HCN) model family.
With all the above work addressing goal-oriented dialogue, our final contribution in this thesis
focuses on social dialogue where the main objective is maintaining natural, coherent, and engaging conversation for as long as possible. We introduce a neural model for response ranking
in social conversation used in Alana, the 3rd place winner in the Amazon Alexa Prize 2017 and
2018. For our model, we employ a novel technique of predicting the dialogue length as the main
objective for ranking. We show that this approach matches the performance of its counterpart
based on the conventional, human rating-based objective — and surpasses it given more raw
dialogue transcripts, thus reducing the dependence on costly and cumbersome dialogue annotations.EPSRC project BABBLE (grant EP/M01553X/1)
LexRank: Graph-based Lexical Centrality as Salience in Text Summarization
We introduce a stochastic graph-based method for computing relative
importance of textual units for Natural Language Processing. We test the
technique on the problem of Text Summarization (TS). Extractive TS relies on
the concept of sentence salience to identify the most important sentences in a
document or set of documents. Salience is typically defined in terms of the
presence of particular important words or in terms of similarity to a centroid
pseudo-sentence. We consider a new approach, LexRank, for computing sentence
importance based on the concept of eigenvector centrality in a graph
representation of sentences. In this model, a connectivity matrix based on
intra-sentence cosine similarity is used as the adjacency matrix of the graph
representation of sentences. Our system, based on LexRank ranked in first place
in more than one task in the recent DUC 2004 evaluation. In this paper we
present a detailed analysis of our approach and apply it to a larger data set
including data from earlier DUC evaluations. We discuss several methods to
compute centrality using the similarity graph. The results show that
degree-based methods (including LexRank) outperform both centroid-based methods
and other systems participating in DUC in most of the cases. Furthermore, the
LexRank with threshold method outperforms the other degree-based techniques
including continuous LexRank. We also show that our approach is quite
insensitive to the noise in the data that may result from an imperfect topical
clustering of documents
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