265 research outputs found
Attention-Informed Mixed-Language Training for Zero-shot Cross-lingual Task-oriented Dialogue Systems
Recently, data-driven task-oriented dialogue systems have achieved promising
performance in English. However, developing dialogue systems that support
low-resource languages remains a long-standing challenge due to the absence of
high-quality data. In order to circumvent the expensive and time-consuming data
collection, we introduce Attention-Informed Mixed-Language Training (MLT), a
novel zero-shot adaptation method for cross-lingual task-oriented dialogue
systems. It leverages very few task-related parallel word pairs to generate
code-switching sentences for learning the inter-lingual semantics across
languages. Instead of manually selecting the word pairs, we propose to extract
source words based on the scores computed by the attention layer of a trained
English task-related model and then generate word pairs using existing
bilingual dictionaries. Furthermore, intensive experiments with different
cross-lingual embeddings demonstrate the effectiveness of our approach.
Finally, with very few word pairs, our model achieves significant zero-shot
adaptation performance improvements in both cross-lingual dialogue state
tracking and natural language understanding (i.e., intent detection and slot
filling) tasks compared to the current state-of-the-art approaches, which
utilize a much larger amount of bilingual data.Comment: Accepted as an oral presentation in AAAI 202
Dialogue State Induction Using Neural Latent Variable Models
Dialogue state modules are a useful component in a task-oriented dialogue
system. Traditional methods find dialogue states by manually labeling training
corpora, upon which neural models are trained. However, the labeling process
can be costly, slow, error-prone, and more importantly, cannot cover the vast
range of domains in real-world dialogues for customer service. We propose the
task of dialogue state induction, building two neural latent variable models
that mine dialogue states automatically from unlabeled customer service
dialogue records. Results show that the models can effectively find meaningful
slots. In addition, equipped with induced dialogue states, a state-of-the-art
dialogue system gives better performance compared with not using a dialogue
state module.Comment: IJCAI 202
Five sources of bias in natural language processing
Recently, there has been an increased interest in demographically grounded bias in natural language processing (NLP) applications. Much of the recent work has focused on describing bias and providing an overview of bias in a larger context. Here, we provide a simple, actionable summary of this recent work. We outline five sources where bias can occur in NLP systems: (1) the data, (2) the annotation process, (3) the input representations, (4) the models, and finally (5) the research design (or how we conceptualize our research). We explore each of the bias sources in detail in this article, including examples and links to related work, as well as potential counter-measures
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