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Yanran Li

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5 papers

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AAAI Conference 2024 System Paper

NarrativePlay: An Automated System for Crafting Visual Worlds in Novels for Role-Playing

Runcong Zhao
Wenjia Zhang
Jiazheng Li
Lixing Zhu
Yanran Li
Yulan He
Lin Gui

In this demo, we present NarrativePlay -- an innovative system enabling users to role-play a fictional character and interact with dynamically generated narrative environments. Unlike existing predefined sandbox approaches, NarrativePlay centres around the main storyline events extracted from the narrative, allowing users to experience the story from the perspective of a character they chose. To design versatile AI agents for diverse scenarios, we employ a framework built on a Large Language Models (LLMs) to extract detailed character traits from text. We also incorporate automatically generated visual displays of narrative settings, character portraits, and character speech, greatly enhancing the overall user experience.

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AAAI Conference 2023 Conference Paper

BERT-ERC: Fine-Tuning BERT Is Enough for Emotion Recognition in Conversation

Xiangyu Qin
Zhiyu Wu
Tingting Zhang
Yanran Li
Jian Luan
Bin Wang
Li Wang
Jinshi Cui

Previous works on emotion recognition in conversation (ERC) follow a two-step paradigm, which can be summarized as first producing context-independent features via fine-tuning pretrained language models (PLMs) and then analyzing contextual information and dialogue structure information among the extracted features. However, we discover that this paradigm has several limitations. Accordingly, we propose a novel paradigm, i.e., exploring contextual information and dialogue structure information in the fine-tuning step, and adapting the PLM to the ERC task in terms of input text, classification structure, and training strategy. Furthermore, we develop our model BERT-ERC according to the proposed paradigm, which improves ERC performance in three aspects, namely suggestive text, fine-grained classification module, and two-stage training. Compared to existing methods, BERT-ERC achieves substantial improvement on four datasets, indicating its effectiveness and generalization capability. Besides, we also set up the limited resources scenario and the online prediction scenario to approximate real-world scenarios. Extensive experiments demonstrate that the proposed paradigm significantly outperforms the previous one and can be adapted to various scenes.

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AAAI Conference 2023 Conference Paper

MIMO Is All You Need：A Strong Multi-in-Multi-Out Baseline for Video Prediction

Shuliang Ning
Mengcheng Lan
Yanran Li
Chaofeng Chen
Qian Chen
Xunlai Chen
Xiaoguang Han
Shuguang Cui

The mainstream of the existing approaches for video prediction builds up their models based on a Single-In-Single-Out (SISO) architecture, which takes the current frame as input to predict the next frame in a recursive manner. This way often leads to severe performance degradation when they try to extrapolate a longer period of future, thus limiting the practical use of the prediction model. Alternatively, a Multi-In-Multi-Out (MIMO) architecture that outputs all the future frames at one shot naturally breaks the recursive manner and therefore prevents error accumulation. However, only a few MIMO models for video prediction are proposed and they only achieve inferior performance due to the date. The real strength of the MIMO model in this area is not well noticed and is largely under-explored. Motivated by that, we conduct a comprehensive investigation in this paper to thoroughly exploit how far a simple MIMO architecture can go. Surprisingly, our empirical studies reveal that a simple MIMO model can outperform the state-of-the-art work with a large margin much more than expected, especially in dealing with long-term error accumulation. After exploring a number of ways and designs, we propose a new MIMO architecture based on extending the pure Transformer with local spatio-temporal blocks and a new multi-output decoder, namely MIMO-VP, to establish a new standard in video prediction. We evaluate our model in four highly competitive benchmarks. Extensive experiments show that our model wins 1st place on all the benchmarks with remarkable performance gains and surpasses the best SISO model in all aspects including efficiency, quantity, and quality. A dramatic error reduction is achieved when predicting 10 frames on Moving MNIST and Weather datasets respectively. We believe our model can serve as a new baseline to facilitate the future research of video prediction tasks. The code will be released.

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AAAI Conference 2021 Conference Paper

Writing Polishment with Simile: Task, Dataset and A Neural Approach

Jiayi Zhang
Zhi Cui
Xiaoqiang Xia
Yalong Guo
Yanran Li
Chen Wei
Jianwei Cui

A simile is a figure of speech that directly makes a comparison, showing similarities between two different things, e. g. “Reading papers can be dull sometimes, like watching grass grow”. Human writers often interpolate appropriate similes into proper locations of the plain text to vivify their writings. However, none of existing work has explored neural simile interpolation, including both locating and generation. In this paper, we propose a new task of Writing Polishment with Simile (WPS) to investigate whether machines are able to polish texts with similes as we human do. Accordingly, we design a two-staged Locate&Gen model based on transformer architecture. Our model firstly locates where the simile interpolation should happen, and then generates a location-specific simile. We also release a large-scale Chinese Simile (CS) dataset containing 5 million similes with context. The experimental results demonstrate the feasibility of WPS task and shed light on the future research directions towards better automatic text polishment.

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AAAI Conference 2019 Short Paper

Meta-Path Augmented Response Generation

Yanran Li
Wenjie Li

We propose a chatbot, namely MOCHA to make good use of relevant entities when generating responses. Augmented with meta-path information, MOCHA is able to mention proper entities following the conversation flow.

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