Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userPast, Present, and Future: Conversational Emotion Recognition through Structural Modeling of Psychological Knowledge
الماضي، الحاضر، والمستقبل: عاطفة المحادثة الاعتراف من خلال النمذجة الهيكلية للمعرفة النفسية
290
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
تعتبر التعرف على عاطلة المحادثة (CER) مهمة للتنبؤ بمشاعر الكلام في سياق محادثة. على الرغم من أن نمذجة سياق المحادثة والتفاعلات بين المتحدثين قد درست على نطاق واسع، إلا أنه من المهم النظر في الدولة النفسية للمتحدث، والتي تسيطر على عمل ومكبر الصوت. تقدم الطريقة التي من بين الفنون المعرفة المنطقية (CSK) نموذجا نفسيا بطريقة متتالية (إلى الأمام والخلف). ومع ذلك، فإنه يتجاهل التفاعلات النفسية الهيكلية بين الكلام. في هذه الورقة، نقترح رسم بياني تفاعل علمي المعرفة (Skaig). في الرسم البياني المرتبط محليا، سيتم تعزيز النطق المستهدف مع معلومات العمل التي استنتجها من السياق الماضي ونهايها الضمنية السياق المستقبلية. الكلام مرتبط بالنظر في الاعتبار التأثير الحالي من نفسه. علاوة على ذلك، نستخدم CSK لإثراء الحواف بتمثيل المعرفة وعمل Skaig مع محول الرسم البياني. تقوم طريقةنا بتحقيق الأداء الحكومي والتنافسي في أربعة مجموعات بيانات CRES.
Conversational Emotion Recognition (CER) is a task to predict the emotion of an utterance in the context of a conversation. Although modeling the conversational context and interactions between speakers has been studied broadly, it is important to consider the speaker's psychological state, which controls the action and intention of the speaker. The state-of-the-art method introduces CommonSense Knowledge (CSK) to model psychological states in a sequential way (forwards and backwards). However, it ignores the structural psychological interactions between utterances. In this paper, we propose a pSychological-Knowledge-Aware Interaction Graph (SKAIG). In the locally connected graph, the targeted utterance will be enhanced with the information of action inferred from the past context and intention implied by the future context. The utterance is self-connected to consider the present effect from itself. Furthermore, we utilize CSK to enrich edges with knowledge representations and process the SKAIG with a graph transformer. Our method achieves state-of-the-art and competitive performance on four popular CER datasets.
References used
https://aclanthology.org/
rate research
Read More
Identifying relevant knowledge to be used in conversational systems that are grounded in long documents is critical to effective response generation. We introduce a knowledge identification model that leverages the document structure to provide dialo
gue-contextualized passage encodings and better locate knowledge relevant to the conversation. An auxiliary loss captures the history of dialogue-document connections. We demonstrate the effectiveness of our model on two document-grounded conversational datasets and provide analyses showing generalization to unseen documents and long dialogue contexts.
Emotion recognition in conversation has received considerable attention recently because of its practical industrial applications. Existing methods tend to overlook the immediate mutual interaction between different speakers in the speaker-utterance
level, or apply single speaker-agnostic RNN for utterances from different speakers. We propose COIN, a conversational interactive model to mitigate this problem by applying state mutual interaction within history contexts. In addition, we introduce a stacked global interaction module to capture the contextual and inter-dependency representation in a hierarchical manner. To improve the robustness and generalization during training, we generate adversarial examples by applying the minor perturbations on multimodal feature inputs, unveiling the benefits of adversarial examples for emotion detection. The proposed model empirically achieves the current state-of-the-art results on the IEMOCAP benchmark dataset.
Awareness in one of the most important factors for the study of heritage, so as to
find the optimal formula of its comprehensive aspects of scientific, philosophical, social,
political and economic levels, i.e. the ideological and cognitive levels
(philosophical).
These two levels have a large role in determining the central point adopted in determining
the development of theme of heritage, which forms the most important problems of the
current era.
Several recent studies on dyadic human-human interactions have been done on conversations without specific business objectives. However, many companies might benefit from studies dedicated to more precise environments such as after sales services or
customer satisfaction surveys. In this work, we place ourselves in the scope of a live chat customer service in which we want to detect emotions and their evolution in the conversation flow. This context leads to multiple challenges that range from exploiting restricted, small and mostly unlabeled datasets to finding and adapting methods for such context. We tackle these challenges by using Few-Shot Learning while making the hypothesis it can serve conversational emotion classification for different languages and sparse labels. We contribute by proposing a variation of Prototypical Networks for sequence labeling in conversation that we name ProtoSeq. We test this method on two datasets with different languages: daily conversations in English and customer service chat conversations in French. When applied to emotion classification in conversations, our method proved to be competitive even when compared to other ones.
We present Hidden-State Optimization (HSO), a gradient-based method for improving the performance of transformer language models at inference time. Similar to dynamic evaluation (Krause et al., 2018), HSO computes the gradient of the log-probability
the language model assigns to an evaluation text, but uses it to update the cached hidden states rather than the model parameters. We test HSO with pretrained Transformer-XL and GPT-2 language models, finding improvement on the WikiText-103 and PG-19 datasets in terms of perplexity, especially when evaluating a model outside of its training distribution. We also demonstrate downstream applicability by showing gains in the recently developed prompt-based few-shot evaluation setting, again with no extra parameters or training data.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
