@inproceedings{pagarey-etal-2021-stylistic,
title = "Stylistic {MR}-to-Text Generation Using Pre-trained Language Models",
author = "Pagarey, Kunal and
Kalra, Kanika and
Garg, Abhay and
Saha, Saumajit and
Patidar, Mayur and
Karande, Shirish",
editor = "Bandyopadhyay, Sivaji and
Devi, Sobha Lalitha and
Bhattacharyya, Pushpak",
booktitle = "Proceedings of the 18th International Conference on Natural Language Processing (ICON)",
month = dec,
year = "2021",
address = "National Institute of Technology Silchar, Silchar, India",
publisher = "NLP Association of India (NLPAI)",
url = "https://aclanthology.org/2021.icon-main.13",
pages = "93--99",
abstract = "We explore the ability of pre-trained language models BART, an encoder-decoder model, GPT2 and GPT-Neo, both decoder-only models for generating sentences from structured MR tags as input. We observe best results on several metrics for the YelpNLG and E2E datasets. Style based implicit tags such as emotion, sentiment, length etc., allows for controlled generation but it is typically not present in MR. We present an analysis on YelpNLG showing BART can express the content with stylistic variations in the structure of the sentence. Motivated with the results, we define a new task of emotional situation generation from various POS tags and emotion label values as MR using EmpatheticDialogues dataset and report a baseline. Encoder-Decoder attention analysis shows that BART learns different aspects in MR at various layers and heads.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="pagarey-etal-2021-stylistic">
<titleInfo>
<title>Stylistic MR-to-Text Generation Using Pre-trained Language Models</title>
</titleInfo>
<name type="personal">
<namePart type="given">Kunal</namePart>
<namePart type="family">Pagarey</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Kanika</namePart>
<namePart type="family">Kalra</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Abhay</namePart>
<namePart type="family">Garg</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Saumajit</namePart>
<namePart type="family">Saha</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mayur</namePart>
<namePart type="family">Patidar</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Shirish</namePart>
<namePart type="family">Karande</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2021-12</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 18th International Conference on Natural Language Processing (ICON)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Sivaji</namePart>
<namePart type="family">Bandyopadhyay</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sobha</namePart>
<namePart type="given">Lalitha</namePart>
<namePart type="family">Devi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Pushpak</namePart>
<namePart type="family">Bhattacharyya</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>NLP Association of India (NLPAI)</publisher>
<place>
<placeTerm type="text">National Institute of Technology Silchar, Silchar, India</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>We explore the ability of pre-trained language models BART, an encoder-decoder model, GPT2 and GPT-Neo, both decoder-only models for generating sentences from structured MR tags as input. We observe best results on several metrics for the YelpNLG and E2E datasets. Style based implicit tags such as emotion, sentiment, length etc., allows for controlled generation but it is typically not present in MR. We present an analysis on YelpNLG showing BART can express the content with stylistic variations in the structure of the sentence. Motivated with the results, we define a new task of emotional situation generation from various POS tags and emotion label values as MR using EmpatheticDialogues dataset and report a baseline. Encoder-Decoder attention analysis shows that BART learns different aspects in MR at various layers and heads.</abstract>
<identifier type="citekey">pagarey-etal-2021-stylistic</identifier>
<location>
<url>https://aclanthology.org/2021.icon-main.13</url>
</location>
<part>
<date>2021-12</date>
<extent unit="page">
<start>93</start>
<end>99</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Stylistic MR-to-Text Generation Using Pre-trained Language Models
%A Pagarey, Kunal
%A Kalra, Kanika
%A Garg, Abhay
%A Saha, Saumajit
%A Patidar, Mayur
%A Karande, Shirish
%Y Bandyopadhyay, Sivaji
%Y Devi, Sobha Lalitha
%Y Bhattacharyya, Pushpak
%S Proceedings of the 18th International Conference on Natural Language Processing (ICON)
%D 2021
%8 December
%I NLP Association of India (NLPAI)
%C National Institute of Technology Silchar, Silchar, India
%F pagarey-etal-2021-stylistic
%X We explore the ability of pre-trained language models BART, an encoder-decoder model, GPT2 and GPT-Neo, both decoder-only models for generating sentences from structured MR tags as input. We observe best results on several metrics for the YelpNLG and E2E datasets. Style based implicit tags such as emotion, sentiment, length etc., allows for controlled generation but it is typically not present in MR. We present an analysis on YelpNLG showing BART can express the content with stylistic variations in the structure of the sentence. Motivated with the results, we define a new task of emotional situation generation from various POS tags and emotion label values as MR using EmpatheticDialogues dataset and report a baseline. Encoder-Decoder attention analysis shows that BART learns different aspects in MR at various layers and heads.
%U https://aclanthology.org/2021.icon-main.13
%P 93-99
Markdown (Informal)
[Stylistic MR-to-Text Generation Using Pre-trained Language Models](https://aclanthology.org/2021.icon-main.13) (Pagarey et al., ICON 2021)
ACL
- Kunal Pagarey, Kanika Kalra, Abhay Garg, Saumajit Saha, Mayur Patidar, and Shirish Karande. 2021. Stylistic MR-to-Text Generation Using Pre-trained Language Models. In Proceedings of the 18th International Conference on Natural Language Processing (ICON), pages 93–99, National Institute of Technology Silchar, Silchar, India. NLP Association of India (NLPAI).