2 research outputs found
Learning natural language interfaces with neural models
Language is the primary and most natural means of communication for humans. The
learning curve of interacting with various devices and services (e.g., digital assistants,
and smart appliances) would be greatly reduced if we could talk to machines using
human language. However, in most cases computers can only interpret and execute
formal languages. In this thesis, we focus on using neural models to build natural
language interfaces which learn to map naturally worded expressions onto machineinterpretable
representations. The task is challenging due to (1) structural mismatches
between natural language and formal language, (2) the well-formedness of output representations,
(3) lack of uncertainty information and interpretability, and (4) the model
coverage for language variations. In this thesis, we develop several flexible neural
architectures to address these challenges.
We propose a model based on attention-enhanced encoder-decoder neural networks
for natural language interfaces. Beyond sequence modeling, we propose a tree decoder
to utilize the compositional nature and well-formedness of meaning representations,
which recursively generates hierarchical structures in a top-down manner. To model
meaning at different levels of granularity, we present a structure-aware neural architecture
which decodes semantic representations following a coarse-to-fine procedure.
The proposed neural models remain difficult to interpret, acting in most cases as
a black box. We explore ways to estimate and interpret the model’s confidence in its
predictions, which we argue can provide users with immediate and meaningful feedback
regarding uncertain outputs. We estimate confidence scores that indicate whether
model predictions are likely to be correct. Moreover, we identify which parts of the
input contribute to uncertain predictions allowing users to interpret their model.
Model coverage is one of the major reasons resulting in uncertainty of natural language
interfaces. Therefore, we develop a general framework to handle the many
different ways natural language expresses the same information need. We leverage
external resources to generate felicitous paraphrases for the input, and then feed them
to a neural paraphrase scoring model which assigns higher weights to linguistic expressions
most likely to yield correct answers. The model components are trained
end-to-end using supervision signals provided by the target task.
Experimental results show that the proposed neural models can be easily ported
across tasks. Moreover, the robustness of natural language interfaces can be enhanced
by considering the output well-formedness, confidence modeling, and improving model
coverage