Improving Intent Classification in an E-commerce Voice Assistant by Using Inter-Utterance Context

Sharma, Arpit

doi:10.18653/v1/2020.ecnlp-1.6

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…Text and images are linked together using a constrained set of coherence relations, which can summarize the structural, logical and purposeful relationships between the contributions of text and the contributions of images. Examples from the Conceptual Captions dataset (Sharma, 2020 ) that include Creative Commons Licensed image–text pairs.…”

Section: Coherence In Image–text Presentationsmentioning

confidence: 99%

Image–text coherence and its implications for multimodal AI

Alikhani

Khalid

Stone

2023

Front. Artif. Intell.

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Human communication often combines imagery and text into integrated presentations, especially online. In this paper, we show how image–text coherence relations can be used to model the pragmatics of image–text presentations in AI systems. In contrast to alternative frameworks that characterize image–text presentations in terms of the priority, relevance, or overlap of information across modalities, coherence theory postulates that each unit of a discourse stands in specific pragmatic relations to other parts of the discourse, with each relation involving its own information goals and inferential connections. Text accompanying an image may, for example, characterize what's visible in the image, explain how the image was obtained, offer the author's appraisal of or reaction to the depicted situation, and so forth. The advantage of coherence theory is that it provides a simple, robust, and effective abstraction of communicative goals for practical applications. To argue this, we review case studies describing coherence in image–text data sets, predicting coherence from few-shot annotations, and coherence models of image–text tasks such as caption generation and caption evaluation.

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Section: Coherence In Image–text Presentationsmentioning

confidence: 99%

Image–text coherence and its implications for multimodal AI

Alikhani

Khalid

Stone

2023

Front. Artif. Intell.

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“…While (Sunkara et al, 2020) tried to fuse multi-model features into a seq-to-seq LSTM based network. In (Sharma, 2020) cross utterance context was effectively used to perform better intent classification with e-commerce voice assistants.…”

Section: Related Workmentioning

confidence: 99%

ASR Adaptation for E-commerce Chatbots using Cross-Utterance Context and Multi-Task Language Modeling

Shenoy¹,

Bodapati²,

Kirchhoff³

2021

Proceedings of the 4th Workshop on E-Commerce and NLP

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Automatic Speech Recognition (ASR) robustness toward slot entities are critical in ecommerce voice assistants that involve monetary transactions and purchases. Along with effective domain adaptation, it is intuitive that cross utterance contextual cues play an important role in disambiguating domain specific content words from speech. In this paper, we investigate various techniques to improve contextualization, content word robustness and domain adaptation of a Transformer-XL neural language model (NLM) to rescore ASR N-best hypotheses. To improve contextualization, we utilize turn level dialogue acts along with cross utterance context carry over. Additionally, to adapt our domaingeneral NLM towards e-commerce on-the-fly, we use embeddings derived from a finetuned masked LM on in-domain data. Finally, to improve robustness towards in-domain content words, we propose a multi-task model that can jointly perform content word detection and language modeling tasks. Compared to a noncontextual LSTM LM baseline, our best performing NLM rescorer results in a content WER reduction of 19.2% on e-commerce audio test set and a slot labeling F1 improvement of 6.4%.

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Improving Intent Classification in an E-commerce Voice Assistant by Using Inter-Utterance Context

Cited by 2 publications

References 12 publications

Image–text coherence and its implications for multimodal AI

Image–text coherence and its implications for multimodal AI

ASR Adaptation for E-commerce Chatbots using Cross-Utterance Context and Multi-Task Language Modeling

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