Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Investigating Effect of Dialogue History in Multilingual Task Oriented
  Dialogue Systems   While the English virtual assistants have achieved exciting performance with
an enormous amount of training resources, the needs of non-English-speakers
have not been satisfied well. Up to Dec 2021, Alexa, one of the most popular
smart speakers around the world, is able to support 9 different languages [1],
while there are thousands of languages in the world, 91 of which are spoken by
more than 10 million people according to statistics published in 2019 [2].
However, training a virtual assistant in other languages than English is often
more difficult, especially for those low-resource languages. The lack of
high-quality training data restricts the performance of models, resulting in
poor user satisfaction. Therefore, we devise an efficient and effective
training solution for multilingual task-orientated dialogue systems, using the
same dataset generation pipeline and end-to-end dialogue system architecture as
BiToD[5], which adopted some key design choices for a minimalistic natural
language design where formal dialogue states are used in place of natural
language inputs. This reduces the room for error brought by weaker natural
language models, and ensures the model can correctly extract the essential slot
values needed to perform dialogue state tracking (DST). Our goal is to reduce
the amount of natural language encoded at each turn, and the key parameter we
investigate is the number of turns (H) to feed as history to model. We first
explore the turning point where increasing H begins to yield limiting returns
on the overall performance. Then we examine whether the examples a model with
small H gets wrong can be categorized in a way for the model to do few-shot
finetuning on. Lastly, will explore the limitations of this approach, and
whether there is a certain type of examples that this approach will not be able
to resolve.

5
The categories of this paper are: gr-qc
21
The categories of this paper are: adap-org
3
The categories of this paper are: physics
25
The categories of this paper are: patt-sol
34
The categories of this paper are: chem-ph
6
The categories of this paper are: astro-ph
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The categories of this paper are: q-fin
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The categories of this paper are: q-bio
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The categories of this paper are: cond-mat
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The categories of this paper are: econ
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The categories of this paper are: nucl-th
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The categories of this paper are: solv-int
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The categories of this paper are: dg-ga
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The categories of this paper are: hep-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: hep-lat