Papers by Mark Riedl
Just Say No: Analyzing the Stance of Neural Dialogue Generation in Offensive Contexts (2021.emnlp-main)
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| Challenge: | despite progress toward data-driven conversational agents, dialogue models still suffer from issues surrounding safety and offensive language. |
| Approach: | They analyze reddit threads and reddits to determine the stance of offensive dialogue models . they find 42% of human responses agree with toxic comments, compared to 13% with safe comments . |
| Outcome: | The proposed model produces 29% fewer offensive replies than the baseline model. |
A Simple and Effective Approach to the Story Cloze Test (N18-2)
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| Challenge: | Existing approaches to the Cloze Test that use feature engineering to achieve high accuracy are ignoring the training set and training a model on the validation set. |
| Approach: | They propose a fully-neural approach to the Cloze Test using skip-thought embeddings of the stories in a feed-forward network that achieves close to state-of-the-art performance without any feature engineering. |
| Outcome: | The proposed approach achieves close to state-of-the-art performance on the Cloze Test without any feature engineering. |
Guiding Neural Story Generation with Reader Models (2022.findings-emnlp)
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| Challenge: | Existing systems that generate narratives with neural language models require substantial knowledge engineering of logical constraints, limiting their generality. |
| Approach: | They propose a framework in which a reader model is used to reason about the storyshould progress. |
| Outcome: | The proposed model outperforms baseline models in plot plausibility and staying on topic. |
Few-Shot Dialogue Summarization via Skeleton-Assisted Prompt Transfer in Prompt Tuning (2024.eacl-long)
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Kaige Xie, Tong Yu, Haoliang Wang, Junda Wu, Handong Zhao, Ruiyi Zhang, Kanak Mahadik, Ani Nenkova, Mark Riedl
| Challenge: | Existing prompt transfer techniques lack consideration for dialogue-specific information. |
| Approach: | They propose a method which leverages skeleton generation as extra supervision that functions as a medium connecting the distinct source and target task. |
| Outcome: | The proposed method significantly outperforms baselines on two dialogue summarization benchmarks. |
Transfer in Deep Reinforcement Learning Using Knowledge Graphs (D19-53)
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| Challenge: | Text adventure games provide a stepping stone toward grounding action in language . prior work demonstrated that using a knowledge graph as a state representation facilitates faster control policy learning. |
| Approach: | They propose to use knowledge graphs as a representation for domain knowledge transfer for training text-adventure playing reinforcement learning agents. |
| Outcome: | The proposed methods let us learn a higher-quality control policy faster in text adventure games. |
Playing Text-Adventure Games with Graph-Based Deep Reinforcement Learning (N19-1)
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| Challenge: | Text adventure games provide a platform for exploring reinforcement learning in combinatorial action space, such as natural language. |
| Approach: | They propose a deep reinforcement learning architecture that represents the game state as a knowledge graph which is learned during exploration. |
| Outcome: | The proposed architecture can learn a control policy faster than baseline alternatives. |
Inferring the Reader: Guiding Automated Story Generation with Commonsense Reasoning (2022.findings-emnlp)
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| Challenge: | Existing methods to automate story generation focus on single-character stories and lack basiccommonsense reasoning. |
| Approach: | They propose a commonsense-inference Augmentedneural StoryTelling framework that introduces commonsensical reasoning into the story generation process. |
| Outcome: | The proposed method produces significantly more coherent, on-topic, enjoyable andfluent stories than existing models in both the single-character and two-character settings. |
Calibrating Trust of Multi-Hop Question Answering Systems with Decompositional Probes (2022.findings-emnlp)
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| Challenge: | Recent work in multi-hop QA has shown that performance can be boosted by decomposing questions into simpler, single-hop questions. |
| Approach: | They propose to decompose multi-hop questions into simpler, single-hop ones to create explanations by probing a neural QA model with them. |
| Outcome: | The proposed approach can be used to generate explanations by probing a neural QA model with them. |
Situated Dialogue Learning through Procedural Environment Generation (2022.acl-long)
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| Challenge: | a key hypothesis in the pursuit towards creating goal-driven natural language-based agents is interactivity and environment grounding is critical for effective language learning. |
| Approach: | They augment LIGHT by learning to procedurally generate additional novel textual worlds and quests to create a curriculum of steadily increasing difficulty for training agents. |
| Outcome: | The authors augment LIGHT by learning to procedurally generate additional novel textual worlds and quests to create a curriculum of increasing difficulty for training agents to achieve such goals. |
Creating Suspenseful Stories: Iterative Planning with Large Language Models (2024.eacl-long)
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| Challenge: | Automated story generation has been a challenge in NLP for many years. |
| Approach: | They propose an iterative-prompting-based method that is grounded in two theoretical foundations of story suspense from cognitive psychology and narratology. |
| Outcome: | The proposed method works in a fully zero-shot manner and does not rely on any supervised story corpora. |
Reframing Human-AI Collaboration for Generating Free-Text Explanations (2022.naacl-main)
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| Challenge: | Large language models are capable of generating fluent-appearing text with little task-specific supervision. |
| Approach: | They propose a pipeline that combines GPT-3 with a supervised filter that incorporates binary acceptability judgments from humans in the loop. |
| Outcome: | The proposed model can generate freetext explanations in a fewshot setting with human-written examples. |
Making Large Language Models into World Models with Precondition and Effect Knowledge (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) are not inherently designed to model real-world dynamics, but can be induced to perform two critical world model functions: determining the applicability of an action based on a given world state and predicting the resulting world state upon action execution. |
| Approach: | They propose to use Large Language Models to model world states and preconditions . they validate that precondition and effect knowledge generated by LLMs aligns with human understanding of world dynamics . |
| Outcome: | The proposed model can predict valid actions and state transitions, thereby replicating existing models. |