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Image captioning: Transforming objects into words
Original
Simao Herdade, Armin Kappeler, Kofi Boakye, and Joao Soares. 2019 · 1906
Earlier work this paper cites.
Visualbert: A simple and performant baseline for vision and language
Original
Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, and Kai-Wei Chang. 2019 · 1908
Earlier work this paper cites.
Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks
Original
Jiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee. 2019 · 1908
Earlier work this paper cites.
Simple and effective noisy channel modeling for neural machine translation
Original
Kyra Yee, Nathan Ng, Yann N Dauphin, and Michael Auli. 2019 · 1908
Earlier work this paper cites.
Encoder-agnostic adaptation for conditional language generation
Original
Zachary M Ziegler, Luke Melas-Kyriazi, Sebastian Gehrmann, and Alexander M Rush. 2019b · 1908
Earlier work this paper cites.
The woman worked as a babysitter: On biases in language generation
Original
Emily Sheng, Kai-Wei Chang, Premkumar Natarajan, and Nanyun Peng. 2019 · 1909
Earlier work this paper cites.
Fine-tuning language models from human preferences
Original
Daniel M Ziegler, Nisan Stiennon, Jeffrey Wu, Tom B Brown, Alec Radford, Dario Amodei, Paul Christiano, and Geoffrey Irving. 2019a · 1909
Earlier work this paper cites.
Plug and play language models: A simple approach to controlled text generation
Original
Sumanth Dathathri, Andrea Madotto, Janice Lan, Jane Hung, Eric Frank, Piero Molino, Jason Yosinski, and Rosanne Liu. 2019 · 1912
Earlier work this paper cites.
Interpolated estimation of markov source parameters from sparse data
Frederick Jelinek. 1980 · 1980
Earlier work this paper cites.
Reducing non-normative text generation from language models
Original
Xiangyu Peng, Siyan Li, Spencer Frazier, and Mark Riedl. 2020 · 2001
Earlier work this paper cites.
Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
Earlier work this paper cites.

Similar

A neural probabilistic language model
Yoshua Bengio, Réjean Ducharme, Pascal Vincent, and Christian Janvin. 2003 · 2003
Cited alongside, same era.
Rouge: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
Cited alongside, same era.
Gedi: Generative discriminator guided sequence generation
Original
Ben Krause, Akhilesh Deepak Gotmare, Bryan McCann, Nitish Shirish Keskar, Shafiq Joty, Richard Socher, and Nazneen Fatema Rajani. 2020 · 2009
Cited alongside, same era.
Neurologic decoding:(un) supervised neural text generation with predicate logic constraints
Original
Ximing Lu, Peter West, Rowan Zellers, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi. 2020 · 2010
Cited alongside, same era.
Meteor universal: Language specific translation evaluation for any target language
Michael Denkowski and Alon Lavie. 2014 · 2014
Cited alongside, same era.

Then

Bottom-up and top-down attention for image captioning and visual question answering
Peter Anderson, Xiaodong He, Chris Buehler, Damien Teney, Mark Johnson, Stephen Gould, and Lei Zhang. 2018 · 2018
Later among the works it cites.
BERT: pre-training of deep bidirectional transformers for language understanding
Original
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
Later among the works it cites.
Pythia v0. 1: the winning entry to the vqa challenge 2018
Original
Yu Jiang, Vivek Natarajan, Xinlei Chen, Marcus Rohrbach, Dhruv Batra, and Devi Parikh. 2018 · 2018
Later among the works it cites.
Deep contextualized word representations
Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
Later among the works it cites.
A corpus for reasoning about natural language grounded in photographs
Original

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Reading the bibliography…

2022

XFBoost: Improving Text Generation with Controllable Decoders

Peng, Xiangyu, Sollami, Michael

Understand

Multimodal conditionality in transformer-based natural language models has demonstrated state-of-the-art performance in the task of product description generation.

Recent approaches condition a language model on one or more images and other textual metadata to achieve near-human performance for describing products from e-commerce stores.
However, generated descriptions may exhibit degrees of inaccuracy or even contradictory claims relative to the inputs of a given product.
In this paper, we propose a controllable language generation framework called Extract-Finetune-Boost (XFBoost), which addresses the problem of inaccurate low-quality inference.

Built on

Image captioning: Transforming objects into words
Original
Simao Herdade, Armin Kappeler, Kofi Boakye, and Joao Soares. 2019 · 1906
Earlier work this paper cites.
Visualbert: A simple and performant baseline for vision and language
Original
Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, and Kai-Wei Chang. 2019 · 1908
Earlier work this paper cites.
Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks
Original
Jiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee. 2019 · 1908
Earlier work this paper cites.
Simple and effective noisy channel modeling for neural machine translation
Original
Kyra Yee, Nathan Ng, Yann N Dauphin, and Michael Auli. 2019 · 1908
Earlier work this paper cites.
Encoder-agnostic adaptation for conditional language generation
Original
Zachary M Ziegler, Luke Melas-Kyriazi, Sebastian Gehrmann, and Alexander M Rush. 2019b · 1908
Earlier work this paper cites.
The woman worked as a babysitter: On biases in language generation
Original
Emily Sheng, Kai-Wei Chang, Premkumar Natarajan, and Nanyun Peng. 2019 · 1909
Earlier work this paper cites.
Fine-tuning language models from human preferences
Original
Daniel M Ziegler, Nisan Stiennon, Jeffrey Wu, Tom B Brown, Alec Radford, Dario Amodei, Paul Christiano, and Geoffrey Irving. 2019a · 1909
Earlier work this paper cites.
Plug and play language models: A simple approach to controlled text generation
Original
Sumanth Dathathri, Andrea Madotto, Janice Lan, Jane Hung, Eric Frank, Piero Molino, Jason Yosinski, and Rosanne Liu. 2019 · 1912
Earlier work this paper cites.
Interpolated estimation of markov source parameters from sparse data
Frederick Jelinek. 1980 · 1980
Earlier work this paper cites.
Reducing non-normative text generation from language models
Original
Xiangyu Peng, Siyan Li, Spencer Frazier, and Mark Riedl. 2020 · 2001
Earlier work this paper cites.
Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
Earlier work this paper cites.

Similar

A neural probabilistic language model
Yoshua Bengio, Réjean Ducharme, Pascal Vincent, and Christian Janvin. 2003 · 2003
Cited alongside, same era.
Rouge: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
Cited alongside, same era.
Gedi: Generative discriminator guided sequence generation
Original
Ben Krause, Akhilesh Deepak Gotmare, Bryan McCann, Nitish Shirish Keskar, Shafiq Joty, Richard Socher, and Nazneen Fatema Rajani. 2020 · 2009
Cited alongside, same era.
Neurologic decoding:(un) supervised neural text generation with predicate logic constraints
Original
Ximing Lu, Peter West, Rowan Zellers, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi. 2020 · 2010
Cited alongside, same era.
Meteor universal: Language specific translation evaluation for any target language
Michael Denkowski and Alon Lavie. 2014 · 2014
Cited alongside, same era.

Then

Bottom-up and top-down attention for image captioning and visual question answering
Peter Anderson, Xiaodong He, Chris Buehler, Damien Teney, Mark Johnson, Stephen Gould, and Lei Zhang. 2018 · 2018
Later among the works it cites.
BERT: pre-training of deep bidirectional transformers for language understanding
Original
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
Later among the works it cites.
Pythia v0. 1: the winning entry to the vqa challenge 2018
Original
Yu Jiang, Vivek Natarajan, Xinlei Chen, Marcus Rohrbach, Dhruv Batra, and Devi Parikh. 2018 · 2018
Later among the works it cites.
Deep contextualized word representations
Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
Later among the works it cites.
A corpus for reasoning about natural language grounded in photographs
Original

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Vqa: Visual question answering

Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C Lawrence Zitnick, and Devi Parikh. 2015 · 2015

Cited alongside, same era.

Microsoft coco captions: Data collection and evaluation server

Original

Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollár, and C Lawrence Zitnick. 2015 · 2015

Cited alongside, same era.

On using monolingual corpora in neural machine translation

Original

Caglar Gulcehre, Orhan Firat, Kelvin Xu, Kyunghyun Cho, Loic Barrault, Huei-Chi Lin, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2015 · 2015

Cited alongside, same era.

Deep residual learning for image recognition

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016 · 2016

Cited alongside, same era.

Stacked attention networks for image question answering

Zichao Yang, Xiaodong He, Jianfeng Gao, Li Deng, and Alex Smola. 2016 · 2016

Cited alongside, same era.

Cold fusion: Training seq2seq models together with language models

Original

Anuroop Sriram, Heewoo Jun, Sanjeev Satheesh, and Adam Coates. 2017 · 2017

Cited alongside, same era.

Alane Suhr, Stephanie Zhou, Ally Zhang, Iris Zhang, Huajun Bai, and Yoav Artzi. 2018 · 2018