Recently, many task-oriented dialogue systems
need to serve users in different languages. However,
it is time-consuming to collect enough
data of each language for training. Thus, zeroshot
adaptation of cross-lingual task-oriented
dialog systems has been studied. Most of existing
methods consider the word-level alignments
to conduct two main tasks for taskoriented
dialogue system, i.e., intent detection
and slot filling, and they rarely explore the dependency
relations among these two tasks. In
this paper, we propose a hierarchical framework
to classify the pre-defined intents in the
high-level and fulfill slot filling under the guidance
of intent in the low-level. Particularly, we
incorporate sentence-level alignment among
different languages to enhance the performance
of intent detection. The extensive experiments
report that our proposed method achieves the
SOTA performance on the public task-oriented
dialog dataset.

HCLD: A Hierarchical Framework for Zero-shot Cross-lingual Dialogue System

Multi-source neural machine translation aims to translate from parallel sources of information (e.g. languages, images, etc.) to a single target language, which has shown better performance than most one-to-one systems. Despite the remarkable success of existing models, they usually neglect the fact that multiple source inputs may have inconsistencies. Such differences might bring noise to the task and limit the performance of existing multi-source NMT approaches due to their indiscriminate usage of input sources for target word predictions. In this paper, we attempt to leverage the potential complementary information among distinct sources and alleviate the occasional conﬂicts of them. To accomplish that, we propose a source invariance network to learn the invariant information of parallel sources. Such network can be easily integrated with multi-encoder based multi-source NMT methods (e.g. multi-encoder RNN and transformer) to enhance the translation results. Extensive experiments on two multi-source translation tasks demonstrate that the proposed approach not only achieves clear gains in translation quality but also captures implicit invariance between different sources.

Meet Changes with Constancy: Learning Invariance in Multi-Source Translation

Click on the Live Session Recording to view the session **Chair:** Kim Gerdes

VOS3 - CL and Linguistic Theories, Cognitive Modeling and Psycholinguistics & Low-resourced and less studied languages  

technical paper

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### THE CONFERENCE WE KNOW AND WE WANT.
**COLING**, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes but also in industrial research departments including tech-startups. COLING provides opportunities for all these communities to showcase their exciting discovery.

In fall of 2022, COLING will be held in Gyeongju in a hybrid format. All participants can either present at the venue site or join virtually. As more people get vaccinated, we are happy to provide safer environments for our colleagues. We believe that COLING 2022 will be one of the conferences, free from the pandemic. The hybrid format gives presenters and sponsors a valuable opportunity to promote their companies in both an online and in-person venue. For the first time in a long time, customers can interact with their sponsor's products first-hand. The online venue, too, gives sponsors the chance to network with those unable to attend the in-person session.

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COLING 2022

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

LONG9: Multimodal 1

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes, but also in industrial research departments and in technological startups. COLING conferences provide opportunities for all these communities to showcase their exciting developments.

COLING 2020

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

Jianfeng Liu

2

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HCLD: A Hierarchical Framework for Zero-shot Cross-lingual Dialogue System

Meet Changes with Constancy: Learning Invariance in Multi-Source Translation

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