# Zero-shot Cross-lingual Conversational Semantic Role Labeling

arXiv: https://arxiv.org/abs/2204.04914 · HTML: https://mlsanity.com/paper/2204.04914/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 34 of its 53 references that are in the map.
- First posted on arXiv 11 Apr 2022.

## Builds on (most cited first, 30 of 34)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [Cross-lingual Language Model Pretraining](https://mlsanity.com/paper/1901.07291/) (2019)
- [Multilingual Denoising Pre-training for Neural Machine Translation](https://mlsanity.com/paper/2001.08210/) (2020)
- [Personalizing Dialogue Agents: I have a dog, do you have pets too?](https://mlsanity.com/paper/1801.07243/) (2018)
- [Unified Language Model Pre-training for Natural Language Understanding and Generation](https://mlsanity.com/paper/1905.03197/) (2019)
- [Contrastive Learning with Hard Negative Samples](https://mlsanity.com/paper/2010.04592/) (2020)
- [Word Alignment by Fine-tuning Embeddings on Parallel Corpora](https://mlsanity.com/paper/2101.08231/) (2021)
- [Simple BERT Models for Relation Extraction and Semantic Role Labeling](https://mlsanity.com/paper/1904.05255/) (2019)
- [A Dataset for Document Grounded Conversations](https://mlsanity.com/paper/1809.07358/) (2018)
- [Proactive Human-Machine Conversation with Explicit Conversation Goals](https://mlsanity.com/paper/1906.05572/) (2019)
- [Pre-training Multilingual Neural Machine Translation by Leveraging Alignment Information](https://mlsanity.com/paper/2010.03142/) (2020)
- [Improving Multi-turn Dialogue Modelling with Utterance ReWriter](https://mlsanity.com/paper/1906.07004/) (2019)
- [On Learning Universal Representations Across Languages](https://mlsanity.com/paper/2007.15960/) (2020)
- [Cross-Lingual Semantic Role Labeling with High-Quality Translated Training Corpus](https://mlsanity.com/paper/2004.06295/) (2020)
- [Pretraining Methods for Dialog Context Representation Learning](https://mlsanity.com/paper/1906.00414/) (2019)
- [Incomplete Utterance Rewriting as Semantic Segmentation](https://mlsanity.com/paper/2009.13166/) (2020)
- [Zero-shot Cross-lingual Transfer of Neural Machine Translation with Multilingual Pretrained Encoders](https://mlsanity.com/paper/2104.08757/) (2021)
- [Zero-shot Cross-lingual Dialogue Systems with Transferable Latent Variables](https://mlsanity.com/paper/1911.04081/) (2019)
- [Structural Pre-training for Dialogue Comprehension](https://mlsanity.com/paper/2105.10956/) (2021)
- [Zero-Shot Cross-lingual Semantic Parsing](https://mlsanity.com/paper/2104.07554/) (2021)
- [SARG: A Novel Semi Autoregressive Generator for Multi-turn Incomplete Utterance Restoration](https://mlsanity.com/paper/2008.01474/) (2020)
- [Semantic Role Labeling Guided Multi-turn Dialogue ReWriter](https://mlsanity.com/paper/2010.01417/) (2020)
- [X-SRL: A Parallel Cross-Lingual Semantic Role Labeling Dataset](https://mlsanity.com/paper/2010.01998/) (2020)
- [Designing the Business Conversation Corpus](https://mlsanity.com/paper/2008.01940/) (2020)
- [Zero-shot Neural Transfer for Cross-lingual Entity Linking](https://mlsanity.com/paper/1811.04154/) (2018)
- [Cross-lingual Semantic Role Labeling with Model Transfer](https://mlsanity.com/paper/2008.10284/) (2020)

## Abstract (arXiv)

While conversational semantic role labeling (CSRL) has shown its usefulness on Chinese conversational tasks, it is still under-explored in non-Chinese languages due to the lack of multilingual CSRL annotations for the parser training. To avoid expensive data collection and error-propagation of translation-based methods, we present a simple but effective approach to perform zero-shot cross-lingual CSRL. Our model implicitly learns language-agnostic, conversational structure-aware and semantically rich representations with the hierarchical encoders and elaborately designed pre-training objectives. Experimental results show that our model outperforms all baselines by large margins on two newly collected English CSRL test sets. More importantly, we confirm the usefulness of CSRL to non-Chinese conversational tasks such as the question-in-context rewriting task in English and the multi-turn dialogue response generation tasks in English, German and Japanese by incorporating the CSRL information into the downstream conversation-based models. We believe this finding is significant and will facilitate the research of non-Chinese dialogue tasks which suffer the problems of ellipsis and anaphora.