Papers by Igor Labutov
Learning to Learn Semantic Parsers from Natural Language Supervision (D18-1)
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| Challenge: | Existing logical forms require a user to be familiar with the underlying structure to learn a semantic parser. |
| Approach: | They propose a method for training semantic parsers from natural language feedback . they use natural language inputs to parse feedback to leverage it as a form of supervision . |
| Outcome: | The proposed algorithm learns a semantic parser from users’ corrections expressed in natural language. |
LIA: A Natural Language Programmable Personal Assistant (D18-2)
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| Challenge: | a prototype of an intelligent personal assistant can be programmed using natural language . a user can instruct her assistants using language similar to how humans teach other humans . |
| Approach: | They present LIA, an intelligent personal assistant that can be programmed using natural language. LIA resides on a typical mobile Android device. |
| Outcome: | The proposed system can be programmed using natural language, and it can perceive the external environment through sensors and effectors. |
Multi-Relational Question Answering from Narratives: Machine Reading and Reasoning in Simulated Worlds (P18-1)
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| Challenge: | Question Answering (QA) has primarily focused on knowledge bases or free text as a source of knowledge. |
| Approach: | They propose a task of multi-relational QA over personal narrative using text worlds . they generate and release a lightweight Python-based framework for easily generating additional worlds and narrative . |
| Outcome: | The proposed framework combines elements of structured QA over knowledge bases and unstructured QA . it generates and analyzes five diverse datasets with dynamic narrative . the framework is lightweight and easy to use . |
Zero-shot Learning of Classifiers from Natural Language Quantification (P18-1)
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| Challenge: | Existing methods to learn concepts from natural language are limited or no labeled examples. |
| Approach: | They propose a framework through which a set of explanations of a concept can be used to learn a classifier without access to any labeled examples. |
| Outcome: | The proposed framework outperforms previous approaches for learning with limited data and is comparable with fully supervised classifiers trained from a small number of labeled examples. |
Look-up and Adapt: A One-shot Semantic Parser (D19-1)
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| Challenge: | Current conversational agents such as Siri, Alexa or Google Assistant do not cater to the specific phrasing of a user or the specific action. |
| Approach: | They propose a semantic parser that generalizes to out-of-domain examples by adapting the logical forms of seen utterances to fit an unseen utterant. |
| Outcome: | The proposed parser improves on one-shot parsing by 68.8% compared to baselines . it adapts the logical forms of seen utterances to fit the unseen utterant . |
Learning to Ask for Conversational Machine Learning (D19-1)
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| Challenge: | Empirical analysis across three domains shows that learned question-asking strategies expedite classifier training by asking appropriate questions at different points in the learning process. |
| Approach: | They propose a reinforcement learning framework where the learner’s actions correspond to question types and the reward for asking a question is based on how the teacher’s response changes performance of the resulting machine learning model. |
| Outcome: | The proposed framework outperforms a random policy on learning classification tasks, but the dialog looks contrived from a human perspective. |