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Yunyi Yang

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3 papers

2 author rows

AAAI Conference 2021 Conference Paper

Multi-Document Transformer for Personality Detection

Feifan Yang
Xiaojun Quan
Yunyi Yang
Jianxing Yu

Personality detection aims to identify the personality traits implied in social media posts. The core of this task is to put together information in multiple scattered posts to depict an overall personality profile for each user. Existing approaches either encode each post individually or assemble posts arbitrarily into a new document that can be encoded sequentially or hierarchically. While the first approach ignores the connection between posts, the second tends to introduce unnecessary post-order bias into posts. In this paper, we propose a multidocument Transformer, namely Transformer-MD, to tackle the above issues. When encoding each post, Transformer-MD allows access to information in the other posts of the user through Transformer-XL’s memory tokens which share the same position embedding. Besides, personality is usually defined along different traits and each trait may need to attend to different post information, which has rarely been touched by existing research. To address this concern, we propose a dimension attention mechanism on top of Transformer-MD to obtain trait-specific representations for multi-trait personality detection. We evaluate the proposed model on the Kaggle and Pandora MBTI datasets and the experimental results show that it compares favorably with baseline methods.

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AAAI Conference 2021 Conference Paper

UBAR: Towards Fully End-to-End Task-Oriented Dialog System with GPT-2

Yunyi Yang
Yunhao Li
Xiaojun Quan

This paper presents our task-oriented dialog system UBAR which models task-oriented dialogs on a dialog session level. Specifically, UBAR is acquired by fine-tuning the large pretrained unidirectional language model GPT-2 on the sequence of the entire dialog session which is composed of user utterance, belief state, database result, system act, and system response of every dialog turn. Additionally, UBAR is evaluated in a more realistic setting, where its dialog context has access to user utterances and all content it generated such as belief states, system acts, and system responses. Experimental results on the MultiWOZ datasets show that UBAR achieves state-of-the-art performances in multiple settings, improving the combined score of response generation, policy optimization, and end-to-end modeling by 4. 7, 3. 5, and 9. 4 points respectively. Thorough analyses demonstrate that the sessionlevel training sequence formulation and the generated dialog context are essential for UBAR to operate as a fully end-toend task-oriented dialog system in real life. We also examine the transfer ability of UBAR to new domains with limited data and provide visualization and a case study to illustrate the advantages of UBAR in modeling on a dialog session level. 1

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ICRA Conference 2019 Conference Paper

DSNet: Joint Learning for Scene Segmentation and Disparity Estimation

Wujing Zhan
Xinqi Ou
Yunyi Yang
Long Chen 0005

Recently, research works have attempted the joint prediction of scene semantics and optical flow estimation, which demonstrate the mutual improvement between both tasks. Besides, the depth information is also indispensable for the scene understanding, and disparity estimation is necessary for outputting dense depth maps. Such task shares a great similarity with the optical flow estimation since they can all be cast into a problem of capturing the difference at a location of two image frames. However, as far as we know, currently there are few networks for the joint learning of semantic and disparity. Moreover, since deep semantic information and disparity feature maps can learn from each other, we find it unnecessary with two independent encoding modules to separately extract semantic and disparity features. Therefore, we propose a unified multi-tasking architecture DSNet, for the simultaneous estimation of semantic and disparity information. In our model, semantic features, extracted by the encoding module ResNet from the left and right images, are used to obtain the deep disparity features via a novel matching module which performs pixel-to-pixel matching. In addition, we also use the disparity map to perform warp operation on deep features of the right image to deal with the problem of lacking of semantic labels. The effectiveness of our method is demonstrated by extensive experiments.

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