Lara J. Martin

• Doctor of Philosophy
• Assistant Professor at University of Maryland, Baltimore County

Although substantial progress has been achieved in speech-to-speech translation systems over the last few years, such systems still require that the speech be written in some appropriate orthography. As speech may differ greatly from the standardized written form of a language, it can be non-trivial to collect written data when there is no standard...

Automated story generation is the problem of automatically selecting a sequence of events, actions, or words that can be told as a story. We seek to develop a system that can generate stories by learning everything it needs to know from textual story corpora. To date, recurrent neural networks that learn language models at character, word, or sente...

Language-modeling--based approaches to story plot generation attempt to construct a plot by sampling from a language model (LM) to predict the next character, word, or sentence to add to the story. LM techniques lack the ability to receive guidance from the user to achieve a specific goal, resulting in stories that don't have a clear sense of progr...

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 sequen...

Natural Language Processing (NLP) techniques are being used more frequently to improve high-tech Augmentative and Alternative Communication (AAC), but many of these techniques are integrated without the inclusion of the users' perspectives. As many of these tools are created with children in mind, autistic adults are often neglected in the design o...

WHAT-IF -- Writing a Hero's Alternate Timeline through Interactive Fiction -- is a system that uses zero-shot meta-prompting to create branching narratives from a prewritten story. Played as an interactive fiction (IF) game, WHAT-IF lets the player choose between decisions that the large language model (LLM) GPT-4 generates as possible branches in...

The role of a Dungeon Master, or DM, in the game Dungeons & Dragons is to perform multiple tasks simultaneously. The DM must digest information about the game setting and monsters, synthesize scenes to present to other players, and respond to the players' interactions with the scene. Doing all of these tasks while maintaining consistency within the...

The role of a Dungeon Master, or DM, in the game Dungeons & Dragons is to perform multiple tasks simultaneously. The DM must digest information about the game setting and monsters, synthesize scenes to present to other players, and respond to the players' interactions with the scene. Doing all of these tasks while maintaining consistency within the...

In the r/AmITheAsshole subreddit, people anonymously share first person narratives that contain some moral dilemma or conflict and ask the community to judge who is at fault (i.e., who is "the asshole"). These first person narratives are, in general, a unique storytelling domain where the author is not only the narrator (the person telling the stor...

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 gam...

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 sy...

In the r/AmITheAsshole subreddit, people anonymously share first person narratives that contain some moral dilemma or conflict and ask the community to judge who is at fault (i.e., who is "the asshole"). In general, first person narratives are a unique storytelling domain where the author is the narrator (the person telling the story) but can also...

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 any flaws that the neural networks might have. However, symbolic metho...

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. W...

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. W...

Contextual Commonsense Inference (CCI) is the problem of inferring causal relations between the events of a text, such as a story. Like other commonsense reasoning tasks, CCI is a problem of language understanding, rather than language generation. We show that prior work, in using language generation to perform CCI, trains models that struggle on t...

Transformers have been showing near-human performance on a variety of tasks, but they are not without their limitations. We discuss the issue of conflating results of transformers that are instructed to do multiple tasks simultaneously. In particular, we focus on the domain of commonsense reasoning within story prose, which we call contextual commo...

The advent of large pre-trained generative language models has provided a common framework for AI story generation via sampling the model to create sequences that continue the story. However, sampling alone is insufficient for story generation. In particular, it is hard to direct a language model to create stories to reach a specific goal event. We...

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 sequen...

Open story generation is the problem of automatically creating a story for any domain without retraining. Neural language models can be trained on large corpora across many domains and then used to generate stories. However, stories generated via language models tend to lack direction and coherence. We introduce a policy gradient reinforcement lear...

Automated story generation is the problem of automatically selecting a sequence of events, actions, or words that can be told as a story. We seek to develop a system that can generate stories by learning everything it needs to know from textual story corpora. To date, recurrent neural networks that learn language models at character, word, or sente...

The problem of improvisational story generation involves one or more agents collaborating in order to create a story without any advance notice of topic. We present a pipeline for an artificial agent that is capable of improvisational story-telling while collaborating with a human agent. Starting with story corpora, we "eventify" sentences, which c...

Automated story generation is the problem of automatically selecting a sequence of events, actions, or words that can be told as a story. We seek to develop a system that can generate stories by learning everything it needs to know from textual story corpora. To date, recurrent neural networks that learn language models at character, word, or sente...

Improvisational storytelling involves one or more people interacting in real-time to create a story without advanced notice of topic or theme. Human improvisation occurs in an open-world that can be in any state and characters can perform any behaviors expressible through natural language. We propose the grand challenge of computational improvisati...

People sometimes express uncertainty unconsciously in order to add layers of meaning on top of their speech, conveying doubts about the accuracy of the information they are trying to communicate. In this paper, we propose a methodology for annotating uncertainty, which is usually a subjective and expensive process, by using crowdsourcing. In our ex...

We report on our system used in the TRECVID 2014 Multimedia Event Detection (MED) and Multimedia Event Recounting (MER) tasks. On the MED task, the CMU team achieved leading performance in the Semantic Query (SQ), 000Ex, 010Ex and 100Ex settings. Furthermore, SQ and 000Ex runs are significantly better than the submissions from the other teams. We a...

Identifying and understanding the motivations of student leaders from Massively Open Online Course (MOOC) discussion forums provides the key to making the on-line learning environment engaging, col-laborative, and instructive. In this paper , we propose to identify student leaders solely based on textual features, or specifically by analyzing how t...

Laughter is an important non-verbal human social signal. Clarifying the mechanism of laughing would be useful in a variety of studies on health or sociology. In this paper we introduce a non-invasive multisensory system for real-time laughter detection and analysis. We focus only on the audio laughter recognition, present the preliminary results we...

In this paper, we describe the concept of an interactive tool which can be employed to build a dialog system to facilitate pronunciation training in situations where only a few minutes of speech in the target language are available. We leverage recent advances in low-resource speech processing, and envision a tool which will help organizations work...