Search Results for author: Michael White

Found 37 papers, 9 papers with code

Structure-to-Text Generation with Self-Training, Acceptability Classifiers and Context-Conditioning for the GEM Shared Task

no code implementations ACL (GEM) 2021 Shreyan Bakshi, Soumya Batra, Peyman Heidari, Ankit Arun, Shashank Jain, Michael White

We explore the use of self-training and acceptability classifiers with pre-trained models for natural language generation in structure-to-text settings using three GEM datasets (E2E, WebNLG-en, Schema-Guided Dialog).

Text Generation

Neural Methodius Revisited: Do Discourse Relations Help with Pre-Trained Models Too?

1 code implementation INLG (ACL) 2021 Aleksandre Maskharashvili, Symon Stevens-Guille, Xintong Li, Michael White

Recent developments in natural language generation (NLG) have bolstered arguments in favor of re-introducing explicit coding of discourse relations in the input to neural models.

Relation Text Generation

Self-Training for Compositional Neural NLG in Task-Oriented Dialogue

2 code implementations INLG (ACL) 2021 Xintong Li, Symon Stevens-Guille, Aleksandre Maskharashvili, Michael White

Neural approaches to natural language generation in task-oriented dialogue have typically required large amounts of annotated training data to achieve satisfactory performance, especially when generating from compositional inputs.

Text Generation

Generating Discourse Connectives with Pre-trained Language Models: Conditioning on Discourse Relations Helps Reconstruct the PDTB

1 code implementation SIGDIAL (ACL) 2022 Symon Stevens-Guille, Aleksandre Maskharashvili, Xintong Li, Michael White

Our results suggest that including discourse relation information in the input of the model significantly improves the consistency with which it produces a correctly realized discourse relation in the output.

Relation Text Generation

Neural NLG for Methodius: From RST Meaning Representations to Texts

1 code implementation INLG (ACL) 2020 Symon Stevens-Guille, Aleksandre Maskharashvili, Amy Isard, Xintong Li, Michael White

While classic NLG systems typically made use of hierarchically structured content plans that included discourse relations as central components, more recent neural approaches have mostly mapped simple, flat inputs to texts without representing discourse relations explicitly.


Leveraging Large Pretrained Models for WebNLG 2020

1 code implementation ACL (WebNLG, INLG) 2020 Xintong Li, Aleksandre Maskharashvili, Symon Jory Stevens-Guille, Michael White

In this paper, we report experiments on finetuning large pretrained models to realize resource description framework (RDF) triples to natural language.

Building Adaptive Acceptability Classifiers for Neural NLG

no code implementations EMNLP 2021 Soumya Batra, Shashank Jain, Peyman Heidari, Ankit Arun, Catharine Youngs, Xintong Li, Pinar Donmez, Shawn Mei, Shiunzu Kuo, Vikas Bhardwaj, Anuj Kumar, Michael White

We propose a novel framework to train models to classify acceptability of responses generated by natural language generation (NLG) models, improving upon existing sentence transformation and model-based approaches.

Sentence Synthetic Data Generation +1

When is Tree Search Useful for LLM Planning? It Depends on the Discriminator

1 code implementation16 Feb 2024 Ziru Chen, Michael White, Raymond Mooney, Ali Payani, Yu Su, Huan Sun

In this paper, we examine how large language models (LLMs) solve multi-step problems under a language agent framework with three components: a generator, a discriminator, and a planning method.

Mathematical Reasoning Re-Ranking +2

Text-to-SQL Error Correction with Language Models of Code

1 code implementation22 May 2023 Ziru Chen, Shijie Chen, Michael White, Raymond Mooney, Ali Payani, Jayanth Srinivasa, Yu Su, Huan Sun

Thus, we propose a novel representation for SQL queries and their edits that adheres more closely to the pre-training corpora of language models of code.

SQL Parsing Text-To-SQL

GEMv2: Multilingual NLG Benchmarking in a Single Line of Code

no code implementations22 Jun 2022 Sebastian Gehrmann, Abhik Bhattacharjee, Abinaya Mahendiran, Alex Wang, Alexandros Papangelis, Aman Madaan, Angelina McMillan-Major, Anna Shvets, Ashish Upadhyay, Bingsheng Yao, Bryan Wilie, Chandra Bhagavatula, Chaobin You, Craig Thomson, Cristina Garbacea, Dakuo Wang, Daniel Deutsch, Deyi Xiong, Di Jin, Dimitra Gkatzia, Dragomir Radev, Elizabeth Clark, Esin Durmus, Faisal Ladhak, Filip Ginter, Genta Indra Winata, Hendrik Strobelt, Hiroaki Hayashi, Jekaterina Novikova, Jenna Kanerva, Jenny Chim, Jiawei Zhou, Jordan Clive, Joshua Maynez, João Sedoc, Juraj Juraska, Kaustubh Dhole, Khyathi Raghavi Chandu, Laura Perez-Beltrachini, Leonardo F. R. Ribeiro, Lewis Tunstall, Li Zhang, Mahima Pushkarna, Mathias Creutz, Michael White, Mihir Sanjay Kale, Moussa Kamal Eddine, Nico Daheim, Nishant Subramani, Ondrej Dusek, Paul Pu Liang, Pawan Sasanka Ammanamanchi, Qi Zhu, Ratish Puduppully, Reno Kriz, Rifat Shahriyar, Ronald Cardenas, Saad Mahamood, Salomey Osei, Samuel Cahyawijaya, Sanja Štajner, Sebastien Montella, Shailza, Shailza Jolly, Simon Mille, Tahmid Hasan, Tianhao Shen, Tosin Adewumi, Vikas Raunak, Vipul Raheja, Vitaly Nikolaev, Vivian Tsai, Yacine Jernite, Ying Xu, Yisi Sang, Yixin Liu, Yufang Hou

This problem is especially pertinent in natural language generation which requires ever-improving suites of datasets, metrics, and human evaluation to make definitive claims.

Benchmarking Text Generation

Towards Transparent Interactive Semantic Parsing via Step-by-Step Correction

1 code implementation Findings (ACL) 2022 Lingbo Mo, Ashley Lewis, Huan Sun, Michael White

In this work, we investigate an interactive semantic parsing framework that explains the predicted logical form step by step in natural language and enables the user to make corrections through natural-language feedback for individual steps.

Question Answering Semantic Parsing

The OSU/Facebook Realizer for SRST 2019: Seq2Seq Inflection and Serialized Tree2Tree Linearization

no code implementations WS 2019 Kartikeya Upasani, David King, Jinfeng Rao, Anusha Balakrishnan, Michael White

We describe our exploratory system for the shallow surface realization task, which combines morphological inflection using character sequence-to-sequence models with a baseline linearizer that implements a tree-to-tree model using sequence-to-sequence models on serialized trees.

Morphological Inflection valid

A Tree-to-Sequence Model for Neural NLG in Task-Oriented Dialog

no code implementations WS 2019 Jinfeng Rao, Kartikeya Upasani, Anusha Balakrishnan, Michael White, Anuj Kumar, Rajen Subba

Generating fluent natural language responses from structured semantic representations is a critical step in task-oriented conversational systems.


Constrained Decoding for Neural NLG from Compositional Representations in Task-Oriented Dialogue

1 code implementation ACL 2019 Anusha Balakrishnan, Jinfeng Rao, Kartikeya Upasani, Michael White, Rajen Subba

Generating fluent natural language responses from structured semantic representations is a critical step in task-oriented conversational systems.


LSTM Hypertagging

no code implementations WS 2018 Reid Fu, Michael White

Hypertagging, or supertagging for surface realization, is the process of assigning lexical categories to nodes in an input semantic graph.

Text Generation

Madly Ambiguous: A Game for Learning about Structural Ambiguity and Why It's Hard for Computers

no code implementations NAACL 2018 Ajda Gokcen, Ethan Hill, Michael White

Madly Ambiguous is an open source, online game aimed at teaching audiences of all ages about structural ambiguity and why it{'}s hard for computers.

Prepositional Phrase Attachment Sentence +1

Combining CNNs and Pattern Matching for Question Interpretation in a Virtual Patient Dialogue System

no code implementations WS 2017 Lifeng Jin, Michael White, Evan Jaffe, Laura Zimmerman, Douglas Danforth

For medical students, virtual patient dialogue systems can provide useful training opportunities without the cost of employing actors to portray standardized patients.


A Corpus of Word-Aligned Asked and Anticipated Questions in a Virtual Patient Dialogue System

no code implementations LREC 2016 Ajda Gokcen, Evan Jaffe, Johnsey Erdmann, Michael White, Douglas Danforth

We present a corpus of virtual patient dialogues to which we have added manually annotated gold standard word alignments.

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