Lara J. Martin


2023

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FIREBALL: A Dataset of Dungeons and Dragons Actual-Play with Structured Game State Information
Andrew Zhu | Karmanya Aggarwal | Alexander Feng | Lara J. Martin | Chris Callison-Burch
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Dungeons & Dragons (D&D) is a tabletop roleplaying game with complex natural language interactions between players and hidden state information. Recent work has shown that large language models (LLMs) that have access to state information can generate higher quality game turns than LLMs that use dialog history alone. However, previous work used game state information that was heuristically created and was not a true gold standard game state. We present FIREBALL, a large dataset containing nearly 25,000 unique sessions from real D&D gameplay on Discord with true game state info. We recorded game play sessions of players who used the Avrae bot, which was developed to aid people in playing D&D online, capturing language, game commands and underlying game state information. We demonstrate that FIREBALL can improve natural language generation (NLG) by using Avrae state information, improving both automated metrics and human judgments of quality. Additionally, we show that LLMs can generate executable Avrae commands, particularly after finetuning.

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Human-in-the-loop Schema Induction
Tianyi Zhang | Isaac Tham | Zhaoyi Hou | Jiaxuan Ren | Leon Zhou | Hainiu Xu | Li Zhang | Lara J. Martin | Rotem Dror | Sha Li | Heng Ji | Martha Palmer | Susan Windisch Brown | Reece Suchocki | Chris Callison-Burch
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)

Schema induction builds a graph representation explaining how events unfold in a scenario. Existing approaches have been based on information retrieval (IR) and information extraction (IE), often with limited human curation. We demonstrate a human-in-the-loop schema induction system powered by GPT-3. We first describe the different modules of our system, including prompting to generate schematic elements, manual edit of those elements, and conversion of those into a schema graph. By qualitatively comparing our system to previous ones, we show that our system not only transfers to new domains more easily than previous approaches, but also reduces efforts of human curation thanks to our interactive interface.

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CoRRPUS: Code-based Structured Prompting for Neurosymbolic Story Understanding
Yijiang River Dong | Lara J. Martin | Chris Callison-Burch
Findings of the Association for Computational Linguistics: ACL 2023

Story generation and understanding—as with all NLG/NLU tasks—has seen a surge in neurosymbolic work. Researchers have recognized that, while large language models (LLMs) have tremendous utility, they can be augmented with symbolic means to be even better and to make up for many flaws that neural networks have. However, symbolic methods are extremely costly in terms of the amount of time and expertise needed to create them. In this work, we capitalize on state-of-the-art Code-LLMs, such as Codex, to bootstrap the use of symbolic methods for tracking the state of stories and aiding in story understanding. We show that our CoRRPUS system and abstracted prompting procedures can beat current state-of-the-art structured LLM techniques on pre-existing story understanding tasks (bAbI Task 2 and Re³) with minimal hand engineering. This work highlights the usefulness of code-based symbolic representations for enabling LLMs to better perform story reasoning tasks.

2022

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Dungeons and Dragons as a Dialog Challenge for Artificial Intelligence
Chris Callison-Burch | Gaurav Singh Tomar | Lara J. Martin | Daphne Ippolito | Suma Bailis | David Reitter
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

AI researchers have posited Dungeons and Dragons (D&D) as a challenge problem to test systems on various language-related capabilities. In this paper, we frame D&D specifically as a dialogue system challenge, where the tasks are to both generate the next conversational turn in the game and predict the state of the game given the dialogue history. We create a gameplay dataset consisting of nearly 900 games, with a total of 7,000 players, 800,000 dialogue turns, 500,000 dice rolls, and 58 million words. We automatically annotate the data with partial state information about the game play. We train a large language model (LM) to generate the next game turn, conditioning it on different information. The LM can respond as a particular character or as the player who runs the game—i.e., the Dungeon Master (DM). It is trained to produce dialogue that is either in-character (roleplaying in the fictional world) or out-of-character (discussing rules or strategy). We perform a human evaluation to determine what factors make the generated output plausible and interesting. We further perform an automatic evaluation to determine how well the model can predict the game state given the history and examine how well tracking the game state improves its ability to produce plausible conversational output.

2021

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Proceedings of the Third Workshop on Narrative Understanding
Nader Akoury | Faeze Brahman | Snigdha Chaturvedi | Elizabeth Clark | Mohit Iyyer | Lara J. Martin
Proceedings of the Third Workshop on Narrative Understanding

2020

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Proceedings of the First Joint Workshop on Narrative Understanding, Storylines, and Events
Claire Bonial | Tommaso Caselli | Snigdha Chaturvedi | Elizabeth Clark | Ruihong Huang | Mohit Iyyer | Alejandro Jaimes | Heng Ji | Lara J. Martin | Ben Miller | Teruko Mitamura | Nanyun Peng | Joel Tetreault
Proceedings of the First Joint Workshop on Narrative Understanding, Storylines, and Events

2019

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Guided Neural Language Generation for Automated Storytelling
Prithviraj Ammanabrolu | Ethan Tien | Wesley Cheung | Zhaochen Luo | William Ma | Lara J. Martin | Mark o. Riedl
Proceedings of the Second Workshop on Storytelling

Neural network based approaches to automated story plot generation attempt to learn how to generate novel plots from a corpus of natural language plot summaries. Prior work has shown that a semantic abstraction of sentences called events improves neural plot generation and and allows one to decompose the problem into: (1) the generation of a sequence of events (event-to-event) and (2) the transformation of these events into natural language sentences (event-to-sentence). However, typical neural language generation approaches to event-to-sentence can ignore the event details and produce grammatically-correct but semantically-unrelated sentences. We present an ensemble-based model that generates natural language guided by events. Our method outperforms the baseline sequence-to-sequence model. Additionally, we provide results for a full end-to-end automated story generation system, demonstrating how our model works with existing systems designed for the event-to-event problem.