Proceedings of the Massively Multilingual Natural Language Understanding Workshop (MMNLU-22)最新文献

Fine-grained Multi-lingual Disentangled Autoencoder for Language-agnostic Representation Learning 用于语言不可知表示学习的细粒度多语言解纠缠自编码器

Proceedings of the Massively Multilingual Natural Language Understanding Workshop (MMNLU-22) Pub Date : 1900-01-01 DOI: 10.18653/v1/2022.mmnlu-1.2

Zetian Wu, Zhongkai Sun, Zhengyang Zhao, Sixing Lu, Chengyuan Ma, Chenlei Guo

{"title":"Fine-grained Multi-lingual Disentangled Autoencoder for Language-agnostic Representation Learning","authors":"Zetian Wu, Zhongkai Sun, Zhengyang Zhao, Sixing Lu, Chengyuan Ma, Chenlei Guo","doi":"10.18653/v1/2022.mmnlu-1.2","DOIUrl":"https://doi.org/10.18653/v1/2022.mmnlu-1.2","url":null,"abstract":"Encoding both language-specific and language-agnostic information into a single high-dimensional space is a common practice of pre-trained Multi-lingual Language Models (pMLM). Such encoding has been shown to perform effectively on natural language tasks requiring semantics of the whole sentence (e.g., translation). However, its effectiveness appears to be limited on tasks requiring partial information of the utterance (e.g., multi-lingual entity retrieval, template retrieval, and semantic alignment). In this work, a novel Fine-grained Multilingual Disentangled Autoencoder (FMDA) is proposed to disentangle fine-grained semantic information from language-specific information in a multi-lingual setting. FMDA is capable of successfully extracting the disentangled template semantic and residual semantic representations. Experiments conducted on the MASSIVE dataset demonstrate that the disentangled encoding can boost each other during the training, thus consistently outperforming the original pMLM and the strong language disentanglement baseline on monolingual template retrieval and cross-lingual semantic retrieval tasks across multiple languages.","PeriodicalId":375461,"journal":{"name":"Proceedings of the Massively Multilingual Natural Language Understanding Workshop (MMNLU-22)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114319340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

C5L7: A Zero-Shot Algorithm for Intent and Slot Detection in Multilingual Task Oriented Languages 面向多语言任务的语言意图和插槽检测的零射击算法

Proceedings of the Massively Multilingual Natural Language Understanding Workshop (MMNLU-22) Pub Date : 1900-01-01 DOI: 10.18653/v1/2022.mmnlu-1.7

Jiun-hao Jhan, Qingxiaoyang Zhu, Nehal Bengre, T. Kanungo

{"title":"C5L7: A Zero-Shot Algorithm for Intent and Slot Detection in Multilingual Task Oriented Languages","authors":"Jiun-hao Jhan, Qingxiaoyang Zhu, Nehal Bengre, T. Kanungo","doi":"10.18653/v1/2022.mmnlu-1.7","DOIUrl":"https://doi.org/10.18653/v1/2022.mmnlu-1.7","url":null,"abstract":"Voice assistants are becoming central to our lives. The convenience of using voice assistants to do simple tasks has created an industry for voice-enabled devices like TVs, thermostats, air conditioners, etc. It has also improved the quality of life of elders by making the world more accessible. Voice assistants engage in task-oriented dialogues using machine-learned language understanding models. However, training deep-learned models take a lot of training data, which is time-consuming and expensive. Furthermore, it is even more problematic if we want the voice assistant to understand hundreds of languages. In this paper, we present a zero-shot deep learning algorithm that uses only the English part of the Massive dataset and achieves a high level of accuracy across 51 languages. The algorithm uses a delexicalized translation model to generate multilingual data for data augmentation. The training data is further weighted to improve the accuracy of the worst-performing languages. We report on our experiments with code-switching, word order, multilingual ensemble methods, and other techniques and their impact on overall accuracy.","PeriodicalId":375461,"journal":{"name":"Proceedings of the Massively Multilingual Natural Language Understanding Workshop (MMNLU-22)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129268395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machine Translation for Multilingual Intent Detection and Slots Filling 多语言意图检测与槽位填充的机器翻译

Proceedings of the Massively Multilingual Natural Language Understanding Workshop (MMNLU-22) Pub Date : 1900-01-01 DOI: 10.18653/v1/2022.mmnlu-1.8

Maxime De Bruyn, Ehsan Lotfi, Jeska Buhmann, Walter Daelemans

引用次数: 3

Byte-Level Massively Multilingual Semantic Parsing 字节级大规模多语言语义解析

Proceedings of the Massively Multilingual Natural Language Understanding Workshop (MMNLU-22) Pub Date : 1900-01-01 DOI: 10.18653/v1/2022.mmnlu-1.3

M. Nicosia, Francesco Piccinno

引用次数: 1

Zero-Shot Cross-Lingual Sequence Tagging as Seq2Seq Generation for Joint Intent Classification and Slot Filling 基于Seq2Seq生成的零射跨语言序列标注联合意图分类和槽填充

Proceedings of the Massively Multilingual Natural Language Understanding Workshop (MMNLU-22) Pub Date : 1900-01-01 DOI: 10.18653/v1/2022.mmnlu-1.6

Fei Wang, Kuan-Hao Huang, Anoop Kumar, A. Galstyan, Greg Ver Steeg, Kai-Wei Chang

引用次数: 0

Play música alegre: A Large-Scale Empirical Analysis of Cross-Lingual Phenomena in Voice Assistant Interactions Play música alegre:语音助手交互中跨语言现象的大规模实证分析

Proceedings of the Massively Multilingual Natural Language Understanding Workshop (MMNLU-22) Pub Date : 1900-01-01 DOI: 10.18653/v1/2022.mmnlu-1.5

Donato Crisostomi, Davide Bernardi, Sarah Campbell

{"title":"Play música alegre: A Large-Scale Empirical Analysis of Cross-Lingual Phenomena in Voice Assistant Interactions","authors":"Donato Crisostomi, Davide Bernardi, Sarah Campbell","doi":"10.18653/v1/2022.mmnlu-1.5","DOIUrl":"https://doi.org/10.18653/v1/2022.mmnlu-1.5","url":null,"abstract":"Cross-lingual phenomena are quite common in informal contexts like social media, where users are likely to mix their native language with English or other languages. However, few studies have focused so far on analyzing cross-lingual interactions in voice-assistant data, which present peculiar features in terms of sentence length, named entities, and use of spoken language. Also, little attention has been posed to European countries, where English is frequently used as a second language. In this paper, we present a large-scale empirical analysis of cross-lingual phenomena (code-mixing, linguistic borrowing, foreign named entities) in the interactions with a large-scale voice assistant in European countries. To do this, we first introduce a general, highly-scalable technique to generate synthetic mixed training data annotated with token-level language labels and we train two neural network models to predict them. We evaluate the models both on the synthetic dataset and on a real dataset of code-switched utterances, showing that the best performance is obtained by a character convolution based model. The results of the analysis highlight different behaviors between countries, having Italy with the highest ratio of cross-lingual utterances and Spain with a marked preference in keeping Spanish words. Our research, paired to the increase of the cross-lingual phenomena in time, motivates further research in developing multilingual Natural Language Understanding (NLU) models, which can naturally deal with cross-lingual interactions.","PeriodicalId":375461,"journal":{"name":"Proceedings of the Massively Multilingual Natural Language Understanding Workshop (MMNLU-22)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128514159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0