1 |
Delving Deeper into Cross-lingual Visual Question Answering ...
|
|
|
|
BASE
|
|
Show details
|
|
2 |
Cross-Lingual Dialogue Dataset Creation via Outline-Based Generation ...
|
|
|
|
BASE
|
|
Show details
|
|
3 |
Improving Word Translation via Two-Stage Contrastive Learning ...
|
|
|
|
BASE
|
|
Show details
|
|
6 |
Crossing the Conversational Chasm: A Primer on Natural Language Processing for Multilingual Task-Oriented Dialogue Systems ...
|
|
|
|
Abstract:
In task-oriented dialogue (ToD), a user holds a conversation with an artificial agent to complete a concrete task. Although this technology represents one of the central objectives of AI and has been the focus of ever more intense research and development efforts, it is currently limited to a few narrow domains (e.g., food ordering, ticket booking) and a handful of languages (e.g., English, Chinese). This work provides an extensive overview of existing methods and resources in multilingual ToD as an entry point to this exciting and emerging field. We find that the most critical factor preventing the creation of truly multilingual ToD systems is the lack of datasets in most languages for both training and evaluation. In fact, acquiring annotations or human feedback for each component of modular systems or for data-hungry end-to-end systems is expensive and tedious. Hence, state-of-the-art approaches to multilingual ToD mostly rely on (zero- or few-shot) cross-lingual transfer from resource-rich languages ...
|
|
Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
|
|
URL: https://arxiv.org/abs/2104.08570 https://dx.doi.org/10.48550/arxiv.2104.08570
|
|
BASE
|
|
Hide details
|
|
7 |
Learning Domain-Specialised Representations for Cross-Lingual Biomedical Entity Linking ...
|
|
|
|
BASE
|
|
Show details
|
|
8 |
Combining Deep Generative Models and Multi-lingual Pretraining for Semi-supervised Document Classification ...
|
|
|
|
BASE
|
|
Show details
|
|
9 |
MirrorWiC: On Eliciting Word-in-Context Representations from Pretrained Language Models ...
|
|
|
|
BASE
|
|
Show details
|
|
10 |
Parameter space factorization for zero-shot learning across tasks and languages ...
|
|
|
|
BASE
|
|
Show details
|
|
11 |
MirrorWiC: On Eliciting Word-in-Context Representations from Pretrained Language Models ...
|
|
|
|
BASE
|
|
Show details
|
|
12 |
Learning Domain-Specialised Representations for Cross-Lingual Biomedical Entity Linking ...
|
|
|
|
BASE
|
|
Show details
|
|
13 |
MirrorWiC: On Eliciting Word-in-Context Representations from Pretrained Language Models ...
|
|
|
|
BASE
|
|
Show details
|
|
14 |
Semantic Data Set Construction from Human Clustering and Spatial Arrangement ...
|
|
|
|
BASE
|
|
Show details
|
|
15 |
Fast, Effective, and Self-Supervised: Transforming Masked Language Models into Universal Lexical and Sentence Encoders ...
|
|
|
|
BASE
|
|
Show details
|
|
16 |
Context vs Target Word: Quantifying Biases in Lexical Semantic Datasets ...
|
|
|
|
BASE
|
|
Show details
|
|
17 |
AM2iCo: Evaluating Word Meaning in Context across Low-Resource Languages with Adversarial Examples ...
|
|
|
|
BASE
|
|
Show details
|
|
18 |
Fast, Effective, and Self-Supervised: Transforming Masked Language Models into Universal Lexical and Sentence Encoders ...
|
|
|
|
BASE
|
|
Show details
|
|
19 |
Parameter space factorization for zero-shot learning across tasks and languages
|
|
|
|
In: Transactions of the Association for Computational Linguistics, 9 (2021)
|
|
BASE
|
|
Show details
|
|
20 |
AM2iCo: Evaluating Word Meaning in Context across Low-Resource Languages with Adversarial Examples ...
|
|
|
|
BASE
|
|
Show details
|
|
|
|