18 research outputs found
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Large pre-trained language models have been shown to store factual knowledge
in their parameters, and achieve state-of-the-art results when fine-tuned on
downstream NLP tasks. However, their ability to access and precisely manipulate
knowledge is still limited, and hence on knowledge-intensive tasks, their
performance lags behind task-specific architectures. Additionally, providing
provenance for their decisions and updating their world knowledge remain open
research problems. Pre-trained models with a differentiable access mechanism to
explicit non-parametric memory can overcome this issue, but have so far been
only investigated for extractive downstream tasks. We explore a general-purpose
fine-tuning recipe for retrieval-augmented generation (RAG) -- models which
combine pre-trained parametric and non-parametric memory for language
generation. We introduce RAG models where the parametric memory is a
pre-trained seq2seq model and the non-parametric memory is a dense vector index
of Wikipedia, accessed with a pre-trained neural retriever. We compare two RAG
formulations, one which conditions on the same retrieved passages across the
whole generated sequence, the other can use different passages per token. We
fine-tune and evaluate our models on a wide range of knowledge-intensive NLP
tasks and set the state-of-the-art on three open domain QA tasks, outperforming
parametric seq2seq models and task-specific retrieve-and-extract architectures.
For language generation tasks, we find that RAG models generate more specific,
diverse and factual language than a state-of-the-art parametric-only seq2seq
baseline.Comment: Accepted at NeurIPS 202
Deep Learning for Text Style Transfer: A Survey
Text style transfer is an important task in natural language generation,
which aims to control certain attributes in the generated text, such as
politeness, emotion, humor, and many others. It has a long history in the field
of natural language processing, and recently has re-gained significant
attention thanks to the promising performance brought by deep neural models. In
this paper, we present a systematic survey of the research on neural text style
transfer, spanning over 100 representative articles since the first neural text
style transfer work in 2017. We discuss the task formulation, existing datasets
and subtasks, evaluation, as well as the rich methodologies in the presence of
parallel and non-parallel data. We also provide discussions on a variety of
important topics regarding the future development of this task. Our curated
paper list is at https://github.com/zhijing-jin/Text_Style_Transfer_SurveyComment: Computational Linguistics Journal 202
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)