Automatic Text-based Personality Recognition on Monologues and Multiparty Dialogues Using Attentive Networks and Contextual Embeddings

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AAAI Conference on Artificial Intelligence: Student Abstract and Poster Program Authors: Hang Jiang, Xianzhe Zhang, Jinho D. Choi Year: 2020. https://arxiv.org/abs/1911.09304

Technology

Automatic Text-based Personality Recognition on
Monologues and Multiparty Dialogues
Hang Jiang, Xianzhe Zhang, Jinho D. Choi
{hjian42, xianzhez}@ stanford.edu, jinho.choi@emory.edu
Esssays FriendsPersona
MSF Extraction Algorithm
To build our own dataset, we develop a novel
dialogue extraction algorithm, Main Speaker
Finder (MSF), to extract sub-scenes from full
scenes and mark each sub-scene with a main
speaker for three annotators to annotate.
Annotation through Crowdsourcing
Experiments on Essays
Methods
Experiments on FriendsPersona
Previous works only use simple LIWC-based Models
or Hierarchical CNN. We introduced both attentive
neural networks and contextual word embeddings to
the task of automatic personality prediction:
1. Attention-based CNN (ABCNN)
2. Attention-based LSTM (ABLSTM)
3. Hierarchical Attentive Networks (HAN)
4. Bidirectional Encoder Representations from
Transformers (BERT) and RoBERTa

Distributional Semantic word representation allows Natural Language Processing systems to extract and model an immense amount of information about a language. This technique maps words into a high dimensional continuous space through the use of a single-layer neural network. This process has allowed for advances in many Natural Language Processing research areas and tasks. These representation models are evaluated with the use of analogy tests, questions of the form ``If a is to a' then b is to what?'' are answered by composing multiple word vectors and searching the vector space. During the neural network training process, each word is examined as a member of its context. Generally, a word's context is considered to be the elements adjacent to it within a sentence. While some work has been conducted examining the effect of expanding this definition, very little exploration has been done in this area. Further, no inquiry has been conducted as to the specific linguistic competencies of these models or whether modifying their contexts impacts the information they extract. In this paper we propose a thorough analysis of the various lexical and grammatical competencies of distributional semantic models. We aim to leverage analogy tests to evaluate the most advanced distributional model across 14 different types of linguistic relationships. With this information we will then be able to investigate as to whether modifying the training context renders any differences in quality across any of these categories. Ideally we will be able to identify approaches to training that increase precision in some specific linguistic categories, which will allow us to investigate whether these improvements can be combined by joining the information used in different training approaches to build a single, improved, model.

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Automatic Text-based Personality Recognition on Monologues and Multiparty Dialogues Using Attentive Networks and Contextual Embeddings

1. Automatic Text-based Personality Recognition on Monologues and Multiparty Dialogues Hang Jiang, Xianzhe Zhang, Jinho D. Choi {hjian42, xianzhez}@ stanford.edu, jinho.choi@emory.edu Esssays FriendsPersona MSF Extraction Algorithm To build our own dataset, we develop a novel dialogue extraction algorithm, Main Speaker Finder (MSF), to extract sub-scenes from full scenes and mark each sub-scene with a main speaker for three annotators to annotate. Annotation through Crowdsourcing Experiments on Essays Methods Experiments on FriendsPersona Previous works only use simple LIWC-based Models or Hierarchical CNN. We introduced both attentive neural networks and contextual word embeddings to the task of automatic personality prediction: 1. Attention-based CNN (ABCNN) 2. Attention-based LSTM (ABLSTM) 3. Hierarchical Attentive Networks (HAN) 4. Bidirectional Encoder Representations from Transformers (BERT) and RoBERTa

Automatic Text-based Personality Recognition on Monologues and Multiparty Dialogues Using Attentive Networks and Contextual Embeddings

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