LIA: A Natural Language Programmable Personal Assistant

Labutov, Igor; Srivastava, Shashank; Mitchell, Tom M.

doi:10.18653/v1/d18-2025

Cited by 12 publications

(10 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, most agents do not adapt to the phrasing and interest of a specific end user. It has recently been shown that new functionalities can be added to an agent from user instruction (Azaria et al, 2016;Labutov et al, 2018). However, the user instruction only provides one instance of a general instruction template and the agent is challenged to generalize to variations of the instance given during instruction.…”

Section: Discussionmentioning

confidence: 99%

“…Current commercial conversational agents such as Siri, Alexa or Google Assistant come with a fixed set of simple functions like setting alarms and making reminders, but are often not able to cater to the specific phrasing of a user or the specific action a * These two authors contributed equally user needs. However, it has recently been shown that it is possible to add new functionalities to an agent through natural language instruction (Azaria et al, 2016;Labutov et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Look-up and Adapt: A One-shot Semantic Parser

Arabshahi

Labutov

et al. 2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

Self Cite

View full text Add to dashboard Cite

Computing devices have recently become capable of interacting with their end users via natural language. However, they can only operate within a limited "supported" domain of discourse and fail drastically when faced with an out-of-domain utterance, mainly due to the limitations of their semantic parser. In this paper, we propose a semantic parser that generalizes to out-of-domain examples by learning a general strategy for parsing an unseen utterance through adapting the logical forms of seen utterances, instead of learning to generate a logical form from scratch. Our parser maintains a memory consisting of a representative subset of the seen utterances paired with their logical forms. Given an unseen utterance, our parser works by looking up a similar utterance from the memory and adapting its logical form until it fits the unseen utterance. Moreover, we present a data generation strategy for constructing utterance-logical form pairs from different domains. Our results show an improvement of up to 68.8% on one-shot parsing under two different evaluation settings compared to the baselines.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Look-up and Adapt: A One-shot Semantic Parser

Arabshahi

Labutov

et al. 2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

Self Cite

View full text Add to dashboard Cite

show abstract

“…Srivastava et al (2017) used natural language explanations to teach new concepts. Relatedly, Labutov et al (2018) introduced LIA, a programmable personal assistant that learned from user-provided condition-action rules. Furthermore, Weigelt et al (2020) introduce an approach for teaching systems new programmatic functions from language that explicitly reasons about whether utterances contain "teaching intents," a mechanism that is similar to our procedure for returning NOT-SURE.…”

Section: Related Workmentioning

confidence: 99%

Learning Adaptive Language Interfaces through Decomposition

Karamcheti¹,

Sadigh²,

Liang³

2020

Proceedings of the First Workshop on Interactive and Executable Semantic Parsing

View full text Add to dashboard Cite

Our goal is to create an interactive natural language interface that efficiently and reliably learns from users to complete tasks in simulated robotics settings. We introduce a neural semantic parsing system that learns new high-level abstractions through decomposition: users interactively teach the system by breaking down high-level utterances describing novel behavior into low-level steps that it can understand. Unfortunately, existing methods either rely on grammars which parse sentences with limited flexibility, or neural sequence-to-sequence models that do not learn efficiently or reliably from individual examples. Our approach bridges this gap, demonstrating the flexibility of modern neural systems, as well as the one-shot reliable generalization of grammar-based methods. Our crowdsourced interactive experiments suggest that over time, users complete complex tasks more efficiently while using our system by leveraging what they just taught. At the same time, getting users to trust the system enough to be incentivized to teach high-level utterances is still an ongoing challenge. We end with a discussion of some of the obstacles we need to overcome to fully realize the potential of the interactive paradigm.

show abstract

“…Building AI personal assistants is always a fascinating research topic ever since the middle of last century [1]- [3], [11]. Many successful personal assistant products, such as Apple Siri, Google Now, and Amazon Alexa, emerged in the last decades [5].…”

Section: Introductionmentioning

confidence: 99%

“…Many successful personal assistant products, such as Apple Siri, Google Now, and Amazon Alexa, emerged in the last decades [5]. However, almost all existing AI personal assistants act as service terminals, who answer users' questions, control home automation devices, play music, and even manage users' calendars with verbal commands [7]- [9], [11], [13].…”

Section: Introductionmentioning

confidence: 99%

Table-to-Dialog: Building Dialog Assistants to Chat With People on Behalf of You

2020

View full text Add to dashboard Cite

Artificial Intelligence (AI) personal assistant has attracted much attention from both academia and industry. Almost all existing AI personal assistants serve as service terminals to chat with human users for certain tasks. We are instead interested in building AI personal assistants for a different yet important dialog scenario, where they chat with people to fulfill specific tasks on behalf of their human users. As the personal assistants are playing a requester role, instead of a service terminal role, the conversation goal becomes delivering or requesting information according to specific user requests precisely and efficiently. The challenge for the conversation policy is that all user requests must be delivered precisely, while the challenge for the response generation is that it's generally expected for machine generated responses to cover multiple information slots, either requesting or delivering, to make the conversation efficient. In this paper, we present Table-to-Dialogue, a novel approach to address the above challenges when building a requester role AI personal assistant. We employ an encoder-decoder network to learn explicit conversation policy, which generates the corresponding information slots based on the conversation context and the user request table. We further integrate a novel Multi-Slot Constrained Bi-directional Decoder (MS-CBD) into the above encoder-decoder network, to generate machine response according to the multiple slot values and their intermediate representations from the policy decoder. Different from the existing single direction text decoder approaches, MS-CBD leverage the bi-directional context of the response when generating it to enhance the semantic coherence. The experiments shows that our approach significantly outperform the state-of-the-art conversation approaches on automatic and human evaluation metrics. INDEX TERMS Dialogue system, sequence generation, deep learning, constrained decoding, table-to-text, natural language process.

show abstract

LIA: A Natural Language Programmable Personal Assistant

Cited by 12 publications

References 3 publications

Look-up and Adapt: A One-shot Semantic Parser

Look-up and Adapt: A One-shot Semantic Parser

Learning Adaptive Language Interfaces through Decomposition

Table-to-Dialog: Building Dialog Assistants to Chat With People on Behalf of You

Contact Info

Product

Resources

About