Papers by Ray Shen
Generating Dialogue Responses from a Semantic Latent Space (2020.emnlp-main)
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| Challenge: | Existing models for dialogue generation are unable to integrate information from multiple semantically similar valid responses of a given prompt. |
| Approach: | They propose to learn the pair relationship between the prompts and responses as a regression task instead of the end-to-end classification on vocabulary. |
| Outcome: | The proposed model learns the pair relationship between the prompts and responses on a latent space instead of the end-to-end classification on vocabulary. |
Fast Domain Adaptation of Semantic Parsers via Paraphrase Attention (D19-61)
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| Challenge: | Semantic parsers are used to convert user’s natural language commands to executable logical form in intelligent personal agents. Labeled datasets required to train such parser are expensive to collect, and are never comprehensive. |
| Approach: | They propose to use a sequence-to-sequence/tree attention based attention-based sequence-based parsers which support fast near real time retraining. |
| Outcome: | The proposed parsers can maintain high accuracy and fast retraining time while leveraging paraphrases already present in the training dataset. |
CRUISE: Cold-Start New Skill Development via Iterative Utterance Generation (P18-4)
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| Challenge: | Existing systems require developers to manually generate and annotate a large number of utterances. |
| Approach: | They propose a system that guides ordinary software developers to build a high quality NLU engine from scratch. |
| Outcome: | The proposed system shows that iterative pruning of incorrect utterances reduces human workload and cognitive load. |
Noise Robust Named Entity Understanding for Voice Assistants (2021.naacl-industry)
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Deepak Muralidharan, Joel Ruben Antony Moniz, Sida Gao, Xiao Yang, Justine Kao, Stephen Pulman, Atish Kothari, Ray Shen, Yinying Pan, Vivek Kaul, Mubarak Seyed Ibrahim, Gang Xiang, Nan Dun, Yidan Zhou, Andy O, Yuan Zhang, Pooja Chitkara, Xuan Wang, Alkesh Patel, Kushal Tayal, Roger Zheng, Peter Grasch, Jason D Williams, Lin Li
| Challenge: | Named Entity Recognition and Entity Linking are challenging for voice assistants . utterances are relatively short, so there is not much context to help disambiguate . |
| Approach: | They propose a Named Entity Understanding system that combines NER and EL in a joint reranking module. |
| Outcome: | The proposed framework improves NER accuracy by up to 3.13% and EL accuracy by 3.6% in F1 score . it also leads to better accuracies in other natural language understanding tasks . |
Enhancing the generalization for Intent Classification and Out-of-Domain Detection in SLU (2021.acl-long)
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| Challenge: | Existing methods for intent classification are expensive to collect and train . evaluators have shown that the ability to detect out-of-domain utterances is limited . |
| Approach: | They propose to train a model with only IND data while supporting both intent classification and OOD detection. |
| Outcome: | The proposed model improves on existing models and strong baselines on four datasets. |
SkillBot: Towards Automatic Skill Development via User Demonstration (N19-4)
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| Challenge: | Existing industrial PA products require software developers to build new skills via IDE tools. |
| Approach: | They propose a software that automatically develops a natural language understanding engine and implements the action without the need of coding. |
| Outcome: | The proposed system performs well on both benchmark and in-house datasets. |