A statistical approach for Non-Sentential Utterance Resolution for Interactive QA System

Raghu, Dinesh; Indurthi, Sathish Reddy; Ajmera, Jitendra; Joshi, Sachindra

doi:10.18653/v1/w15-4645

Cited by 13 publications

(16 citation statements)

References 9 publications

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“…Question completion approaches [17,26,28] target this setting by attempting to create full-fledged interrogatives from partial utterances while being independent of the answering resource, but suffer in situations without training pairs and with ad hoc styles. Nevertheless, we try to compare with this line of thought, and show that such completion may not be necessary if the underlying KG can be properly exploited.…”

Section: Related Workmentioning

confidence: 99%

Look before you Hop: Conversational Question Answering over Knowledge Graphs Using Judicious Context Expansion

Christmann

Roy

Abujabal

et al. 2019

Proceedings of the 28th ACM International Conference on Information and Knowledge Management

118

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Fact-centric information needs are rarely one-shot; users typically ask follow-up questions to explore a topic. In such a conversational setting, the user's inputs are often incomplete, with entities or predicates left out, and ungrammatical phrases. This poses a huge challenge to question answering (QA) systems that typically rely on cues in full-fledged interrogative sentences. As a solution, we develop Convex: an unsupervised method that can answer incomplete questions over a knowledge graph (KG) by maintaining conversation context using entities and predicates seen so far and automatically inferring missing or ambiguous pieces for follow-up questions. The core of our method is a graph exploration algorithm that judiciously expands a frontier to find candidate answers for the current question. To evaluate Convex, we release ConvQuestions, a crowdsourced benchmark with 11, 200 distinct conversations from five different domains. We show that Convex: (i) adds conversational support to any stand-alone QA system, and (ii) outperforms state-of-the-art baselines and question completion strategies.

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Section: Related Workmentioning

confidence: 99%

Look before you Hop: Conversational Question Answering over Knowledge Graphs Using Judicious Context Expansion

Christmann

Roy

Abujabal

et al. 2019

Proceedings of the 28th ACM International Conference on Information and Knowledge Management

118

View full text Add to dashboard Cite

show abstract

“…Further, Raghu et al (2015) has explored using context to identify non-sentential utterances (NSUs), defined as utterances that are not full sentences but convey complete meaning in context. The identification of NSUs may improve our model's ability to differentiate between difficult cases (e.g., calling on students, saying a student's name for discipline).…”

Section: Limitations and Future Workmentioning

confidence: 99%

Identifying Teacher Questions Using Automatic Speech Recognition in Classrooms

Blanchard

Donnelly

Olney

et al. 2016

Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue

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We investigate automatic question detection from recordings of teacher speech collected in live classrooms. Our corpus contains audio recordings of 37 class sessions taught by 11 teachers. We automatically segment teacher speech into utterances using an amplitude envelope thresholding approach followed by filtering non-speech via automatic speech recognition (ASR). We manually code the segmented utterances as containing a teacher question or not based on an empirically-validated scheme for coding classroom discourse. We compute domain-independent natural language processing (NLP) features from transcripts generated by three ASR engines (AT&T, Bing Speech, and Azure Speech). Our teacher-independent supervised machine learning model detects questions with an overall weighted F 1 score of 0.59, a 51% improvement over chance. Furthermore, the proportion of automatically-detected questions per class session strongly correlates (Pearson's r = 0.85) with human-coded question rates. We consider our results to reflect a substantial (37%) improvement over the state-of-the-art in automatic question detection from naturalistic audio. We conclude by discussing applications of our work for teachers, researchers, and other stakeholders.

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“…K2Q-Template: For template based approach we use the method proposed by (Zhao et al, 2011) along with the Word2Vec (Mikolov et al, 2015) ranking as proposed by (Raghu et al, 2015). The Word2Vec ranking provides better generalization than the ranking proposed by (Zhao et al, 2011).…”

Section: Methodsmentioning

confidence: 99%

Generating Natural Language Question-Answer Pairs from a Knowledge Graph Using a RNN Based Question Generation Model

Reddy¹,

Raghu²,

Khapra³

et al. 2017

Proceedings of the 15th Conference of the European Chapter of The Association for Computational Linguistics: Volume 1

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In recent years, knowledge graphs such as Freebase that capture facts about entities and relationships between them have been used actively for answering factoid questions. In this paper, we explore the problem of automatically generating question answer pairs from a given knowledge graph. The generated question answer (QA) pairs can be used in several downstream applications. For example, they could be used for training better QA systems. To generate such QA pairs, we first extract a set of keywords from entities and relationships expressed in a triple stored in the knowledge graph. From each such set, we use a subset of keywords to generate a natural language question that has a unique answer. We treat this subset of keywords as a sequence and propose a sequence to sequence model using RNN to generate a natural language question from it. Our RNN based model generates QA pairs with an accuracy of 33.61 percent and performs 110.47 percent (relative) better than a state-of-the-art template based method for generating natural language question from keywords. We also do an extrinsic evaluation by using the generated QA pairs to train a QA system and observe that the F1-score of the QA system improves by 5.5 percent (relative) when using automatically generated QA pairs in addition to manually generated QA pairs available for training.

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A statistical approach for Non-Sentential Utterance Resolution for Interactive QA System

Cited by 13 publications

References 9 publications

Look before you Hop: Conversational Question Answering over Knowledge Graphs Using Judicious Context Expansion

Look before you Hop: Conversational Question Answering over Knowledge Graphs Using Judicious Context Expansion

Identifying Teacher Questions Using Automatic Speech Recognition in Classrooms

Generating Natural Language Question-Answer Pairs from a Knowledge Graph Using a RNN Based Question Generation Model

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