47 citations · 104 across the 14 of their papers we have counts for
14 papers
Benchmarks Underestimate the Readiness of Multi-lingual Dialogue Agents
Andrew H. Lee, Sina J. Semnani, Galo Castillo-López +16
Creating multilingual task-oriented dialogue (TOD) agents is challenging due to the high cost of training data acquisition. Following the research trend of improving training data…
Beyond Performance: Quantifying and Mitigating Label Bias in LLMs
Yuval Reif, Roy Schwartz
Large language models (LLMs) have shown remarkable adaptability to diverse tasks, by leveraging context prompts containing instructions, or minimal input-output examples. However,…
X-RiSAWOZ: High-Quality End-to-End Multilingual Dialogue Datasets and Few-shot Agents
Mehrad Moradshahi, Tianhao Shen, Kalika Bali +16
Task-oriented dialogue research has mainly focused on a few popular languages like English and Chinese, due to the high dataset creation cost for a new language. To reduce the cost…
Zero and Few-Shot Localization of Task-Oriented Dialogue Agents with a Distilled Representation
Mehrad Moradshahi, Sina J. Semnani, Monica S. Lam
Task-oriented Dialogue (ToD) agents are mostly limited to a few widely-spoken languages, mainly due to the high cost of acquiring training data for each language. Existing low-cost…
Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks
Yizhong Wang, Swaroop Mishra, Pegah Alipoormolabashi +37
How well can NLP models generalize to a variety of unseen tasks when provided with task instructions? To address this question, we first introduce Super-NaturalInstructions, a benc…
ThingTalk: An Extensible, Executable Representation Language for Task-Oriented Dialogues
Monica S. Lam, Giovanni Campagna, Mehrad Moradshahi +2
Task-oriented conversational agents rely on semantic parsers to translate natural language to formal representations. In this paper, we propose the design and rationale of the Thin…
Investigating Effect of Dialogue History in Multilingual Task Oriented Dialogue Systems
Michael Sun, Kaili Huang, Mehrad Moradshahi
While the English virtual assistants have achieved exciting performance with an enormous amount of training resources, the needs of non-English-speakers have not been satisfied wel…
Contextual Semantic Parsing for Multilingual Task-Oriented Dialogues
Mehrad Moradshahi, Victoria Tsai, Giovanni Campagna +1
Robust state tracking for task-oriented dialogue systems currently remains restricted to a few popular languages. This paper shows that given a large-scale dialogue data set in one…
Localizing Open-Ontology QA Semantic Parsers in a Day Using Machine Translation
Mehrad Moradshahi, Giovanni Campagna, Sina J. Semnani +2
We propose Semantic Parser Localizer (SPL), a toolkit that leverages Neural Machine Translation (NMT) systems to localize a semantic parser for a new language. Our methodology is t…
Zero-Shot Transfer Learning with Synthesized Data for Multi-Domain Dialogue State Tracking
Giovanni Campagna, Agata Foryciarz, Mehrad Moradshahi +1
Zero-shot transfer learning for multi-domain dialogue state tracking can allow us to handle new domains without incurring the high cost of data acquisition. This paper proposes new…
HUBERT Untangles BERT to Improve Transfer across NLP Tasks
Mehrad Moradshahi, Hamid Palangi, Monica S. Lam +2
We introduce HUBERT which combines the structured-representational power of Tensor-Product Representations (TPRs) and BERT, a pre-trained bidirectional Transformer language model.…
Genie: A Generator of Natural Language Semantic Parsers for Virtual Assistant Commands
Giovanni Campagna, Silei Xu, Mehrad Moradshahi +2
To understand diverse natural language commands, virtual assistants today are trained with numerous labor-intensive, manually annotated sentences. This paper presents a methodology…
Clinical Parameters Prediction for Gait Disorder Recognition
Soheil Esmaeilzadeh, Ouassim Khebzegga, Mehrad Moradshahi
Being able to predict clinical parameters in order to diagnose gait disorders in a patient is of great value in planning treatments. It is known that \textit{decision parameters} s…
Language Modeling with Generative Adversarial Networks
Mehrad Moradshahi, Utkarsh Contractor
Generative Adversarial Networks (GANs) have been promising in the field of image generation, however, they have been hard to train for language generation. GANs were originally des…