Papers by Mauajama Firdaus
On the Way to Gentle AI Counselor: Politeness Cause Elicitation and Intensity Tagging in Code-mixed Hinglish Conversations for Social Good (2024.findings-naacl)
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| Challenge: | Politeness is a multifaceted concept influenced by individual perceptions of what is considered polite or impolite. |
| Approach: | They propose a task to identify the underlying reasons behind the use of politeness and gauge the degree of politity conveyed. |
| Outcome: | The proposed method is compared against state-of-the-art datasets and their results show it is superior. |
EmoInHindi: A Multi-label Emotion and Intensity Annotated Dataset in Hindi for Emotion Recognition in Dialogues (2022.lrec-1)
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| Challenge: | Existing datasets for emotion recognition in dialogues are in English . existing datasets are limited to a few languages like Hindi . |
| Approach: | They propose a large conversational dataset in Hindi for multi-label emotion and intensity recognition in conversations . they use a Wizard-of-Oz manner to annotate dialogues with 16 emotion labels . |
| Outcome: | The proposed dataset contains 1,814 dialogues with 44,247 utterances in Hindi . it is based on a Wizard-of-Oz manner and can detect emotions in conversation . |
A Unified Framework for Emotion Identification and Generation in Dialogues (2023.eacl-srw)
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| Challenge: | Social chatbots have gained immense popularity and can be used to develop and promote social chatbot applications. |
| Approach: | They propose a multi-task framework that jointly identifies the emotion of a given dialogue and generates response in accordance to the identified emotion. |
| Outcome: | The proposed framework outperforms current state-of-the-art models with classification and generation loss. |
MEISD: A Multimodal Multi-Label Emotion, Intensity and Sentiment Dialogue Dataset for Emotion Recognition and Sentiment Analysis in Conversations (2020.coling-main)
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| Challenge: | Emotion and sentiment classification in dialogues has gained popularity in recent times . a number of datasets are imbalanced in representing different emotions and consist of an only single emotion. |
| Approach: | They propose to use a dataset to analyze emotions and sentiments in dialogues . they use text, audio and video to identify the correct emotions with the appropriate intensity and sentiment in an utterance of a dialogue . |
| Outcome: | The proposed datasets are balanced in representing different emotions and consist of only one emotion. |
Ordinal and Attribute Aware Response Generation in a Multimodal Dialogue System (P19-1)
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| Challenge: | Existing multimodal dialogue systems are based on unimodal sources, capturing information from text and image. |
| Approach: | They propose a position and attribute aware attention mechanism to learn enhanced image representation conditioned on the user utterance. |
| Outcome: | The proposed model outperforms the state-of-the-art models on text similarity metrics. |
Incorporating Politeness across Languages in Customer Care Responses: Towards building a Multi-lingual Empathetic Dialogue Agent (2020.lrec-1)
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| Challenge: | Qualitative and quantitative analysis shows that our proposed model can converse in both the languages and the information shared between the languages helps in improving the performance of the overall system. |
| Approach: | They propose a deep learning framework that can handle different languages and incorporate courteous behaviour in generic customer care responses in a multi-lingual scenario. |
| Outcome: | The proposed model can converse in both languages and the information shared between the languages helps in improving the overall performance of the system. |
Deciphering Cognitive Distortions in Patient-Doctor Mental Health Conversations: A Multimodal LLM-Based Detection and Reasoning Framework (2024.emnlp-main)
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| Challenge: | Cognitive distortion research sheds light on pervasive errors in thinking patterns . authors present method for detecting and reasoning about cognitive distortions . |
| Approach: | They propose a method for detecting and reasoning about cognitive distortions using Large Language Models. |
| Outcome: | The proposed method improves accuracy and depth of detection and reasoning tasks in a zero-shot manner. |
Prompt-Based Editing for Text Style Transfer (2023.findings-emnlp)
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| Challenge: | Text style transfer is a type of textual prompt that generates style-transferred texts word by word . early prediction errors may affect future word predictions. |
| Approach: | They propose a prompt-based editing approach to text style transfer using a pretrained language model. |
| Outcome: | The proposed approach outperforms existing systems with 20 times more parameters on three style-transfer benchmark datasets. |
Courteously Yours: Inducing courteous behavior in Customer Care responses using Reinforced Pointer Generator Network (N19-1)
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| Challenge: | In order to ensure customer satisfaction and retention, it is imperative for customer care agents and chatbots to be cordial and emphatic to the customer. |
| Approach: | They propose a deep learning framework that automatically transforms neutral customer care responses into courteous replies by stylistic transfer. |
| Outcome: | The proposed model can generate courteous expressions consistent with the emotional state of the customer while preserving the content. |
PoliSe: Reinforcing Politeness Using User Sentiment for Customer Care Response Generation (2022.coling-1)
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| Challenge: | Human-machine interactions have increased rapidly assisting humans in their everyday lives. |
| Approach: | They propose to automatically identify the sentiment of the user and transform the neutral responses into polite responses conforming to the sentiment and the conversational history. |
| Outcome: | The proposed approach achieves superior performance compared to baseline models. |
MultiDM-GCN: Aspect-guided Response Generation in Multi-domain Multi-modal Dialogue System using Graph Convolutional Network (2020.findings-emnlp)
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| Challenge: | Existing research suggests that engaging conversations include visual cues (e.g., a video or images) or audio cue. |
| Approach: | They propose a multi-modal conversational framework that generates the responses following the different aspects of a product or service to cater to the user's needs. |
| Outcome: | The proposed framework outperforms baselines for the task-oriented dialogue setup. |
Empathetic Persuasion: Reinforcing Empathy and Persuasiveness in Dialogue Systems (2022.findings-naacl)
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| Challenge: | Existing models for persuasive dialogue lack emotion annotated data, so we use transformers to provide emotion based feedbacks to our RL agent. |
| Approach: | They propose to use a language model to generate empathetic persuasive dialogues . they annotate existing data with emotions and build transformers to provide feedbacks based on emotion. |
| Outcome: | The proposed model increases the rate of generating persuasive responses compared to state-of-the-art models while maintaining the language quality. |
Knowledge-enhanced Response Generation in Dialogue Systems: Current Advancements and Emerging Horizons (2024.lrec-tutorials)
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| Challenge: | Knowledge-enhanced Dialogue Systems (KEDS) are a new approach to enhancing human-machine interaction through natural language. |
| Approach: | This tutorial provides an in-depth exploration of Knowledge-enhanced Dialogue Systems (KEDS) it aims to elucidate their significance, highlight advances made using deep learning, and pinpoint the current challenges. |
| Outcome: | The tutorial aims to give attendees a comprehensive understanding of KEDS, and highlight advances made using deep learning and pinpoint the current challenges. |