Papers by Anjali Narayan-Chen
ExPUNations: Augmenting Puns with Keywords and Explanations (2022.emnlp-main)
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Jiao Sun, Anjali Narayan-Chen, Shereen Oraby, Alessandra Cervone, Tagyoung Chung, Jing Huang, Yang Liu, Nanyun Peng
| Challenge: | Puns add the challenge of fusing commonsense and world knowledge with the ability to interpret lexical-semantic ambiguity. |
| Approach: | They propose to augment existing datasets with detailed crowdsourced annotations of puns, keywords and fine-grained funniness ratings to challenge current models' ability to understand and generate humor. |
| Outcome: | The proposed tasks include explanation generation to aid with pun classification and keyword-conditioned pun generation to challenge state-of-the-art models' ability to understand and generate humor. |
Collaborative Dialogue in Minecraft (P19-1)
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| Challenge: | Using computer games to simulate grounded situations, we want to develop interactive agents that can communicate with humans to solve tasks in grounded scenarios. |
| Approach: | They propose a Minecraft-based collaborative building task in which one player is shown a building structure and needs to instruct the other player to build it. |
| Outcome: | The proposed agent can communicate with humans to solve a building task in a Minecraft-based environment without the need for physical robots. |
Unsupervised Melody-to-Lyrics Generation (2023.acl-long)
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Yufei Tian, Anjali Narayan-Chen, Shereen Oraby, Alessandra Cervone, Gunnar Sigurdsson, Chenyang Tao, Wenbo Zhao, Tagyoung Chung, Jing Huang, Nanyun Peng
| Challenge: | Existing methods for automatic melody-to-lyric generation are limited due to the limited amount of melody-lyrical aligned data. |
| Approach: | They propose a method for automatic melody-to-lyric generation without training on any aligned melody-lyr data. |
| Outcome: | The proposed model generates high-quality lyrics that are singable, intelligible, and coherent than baseline models. |
Learning to execute instructions in a Minecraft dialogue (2020.acl-main)
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| Challenge: | Existing attempts to build interactive agents that can communicate with humans about and operate within the physical world are either completely ungrounded, focus on slot-value filling tasks, or operate within static environments, such as images or videos. |
| Approach: | They define the subtask of predicting correct action sequences in a given game context and capture B’s past actions as well as B’ s perspective leads to a significant improvement in performance. |
| Outcome: | The proposed task improves the performance of the two-player game and its corresponding Minecraft Dialogue Corpus. |
Context-Situated Pun Generation (2022.emnlp-main)
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Jiao Sun, Anjali Narayan-Chen, Shereen Oraby, Shuyang Gao, Tagyoung Chung, Jing Huang, Yang Liu, Nanyun Peng
| Challenge: | a new task for context-situated pun generation uses a given context to generate puns . human evaluation shows that 69% of top retrieved pun words can be used to generate context-based puns. |
| Approach: | They propose a task where puns are generated based on contextual keywords and pun words. |
| Outcome: | The proposed system generates successful puns 31% of the time given a plausible tuple of context words and pun pairs. |