20 research outputs found
A Review of Evaluation Techniques for Social Dialogue Systems
In contrast with goal-oriented dialogue, social dialogue has no clear measure
of task success. Consequently, evaluation of these systems is notoriously hard.
In this paper, we review current evaluation methods, focusing on automatic
metrics. We conclude that turn-based metrics often ignore the context and do
not account for the fact that several replies are valid, while end-of-dialogue
rewards are mainly hand-crafted. Both lack grounding in human perceptions.Comment: 2 page
Why We Need New Evaluation Metrics for NLG
The majority of NLG evaluation relies on automatic metrics, such as BLEU . In
this paper, we motivate the need for novel, system- and data-independent
automatic evaluation methods: We investigate a wide range of metrics, including
state-of-the-art word-based and novel grammar-based ones, and demonstrate that
they only weakly reflect human judgements of system outputs as generated by
data-driven, end-to-end NLG. We also show that metric performance is data- and
system-specific. Nevertheless, our results also suggest that automatic metrics
perform reliably at system-level and can support system development by finding
cases where a system performs poorly.Comment: accepted to EMNLP 201
Computer says "No": The Case Against Empathetic Conversational AI
Emotions are an integral part of human cognition and they guide not only our understanding of the world but also our actions within it. As such, whether we soothe or flame an emotion is not inconsequential. Recent work in conversational AI has focused on responding empathetically to users, validating and soothing their emotions without a real basis. This AI-aided emotional regulation can have negative consequences for users and society, tending towards a one-noted happiness defined as only the absence of "negative" emotions. We argue that we must carefully consider whether and how to respond to users' emotions
Angry Men, Sad Women: Large Language Models Reflect Gendered Stereotypes in Emotion Attribution
Large language models (LLMs) reflect societal norms and biases, especially about gender. While societal biases and stereotypes have been extensively researched in various NLP applications, there is a surprising gap for emotion analysis. However, emotion and gender are closely linked in societal discourse. E.g., women are often thought of as more empathetic, while men's anger is more socially accepted. To fill this gap, we present the first comprehensive study of gendered emotion attribution in five state-of-the-art LLMs (open- and closed-source). We investigate whether emotions are gendered, and whether these variations are based on societal stereotypes. We prompt the models to adopt a gendered persona and attribute emotions to an event like 'When I had a serious argument with a dear person'. We then analyze the emotions generated by the models in relation to the gender-event pairs. We find that all models consistently exhibit gendered emotions, influenced by gender stereotypes. These findings are in line with established research in psychology and gender studies. Our study sheds light on the complex societal interplay between language, gender, and emotion. The reproduction of emotion stereotypes in LLMs allows us to use those models to study the topic in detail, but raises questions about the predictive use of those same LLMs for emotion applications
Emotion Analysis in NLP: Trends, Gaps and Roadmap for Future Directions
Emotions are a central aspect of communication. Consequently, emotion analysis (EA) is a rapidly growing field in natural language processing (NLP). However, there is no consensus on scope, direction, or methods. In this paper, we conduct a thorough review of 154 relevant NLP publications from the last decade. Based on this review, we address four different questions: (1) How are EA tasks defined in NLP? (2) What are the most prominent emotion frameworks and which emotions are modeled? (3) Is the subjectivity of emotions considered in terms of demographics and cultural factors? and (4) What are the primary NLP applications for EA? We take stock of trends in EA and tasks, emotion frameworks used, existing datasets, methods, and applications. We then discuss four lacunae: (1) the absence of demographic and cultural aspects does not account for the variation in how emotions are perceived, but instead assumes they are universally experienced in the same manner; (2) the poor fit of emotion categories from the two main emotion theories to the task; (3) the lack of standardized EA terminology hinders gap identification, comparison, and future goals; and (4) the absence of interdisciplinary research isolates EA from insights in other fields. Our work will enable more focused research into EA and a more holistic approach to modeling emotions in NLP
An Ensemble Model with Ranking for Social Dialogue
Open-domain social dialogue is one of the long-standing goals of Artificial
Intelligence. This year, the Amazon Alexa Prize challenge was announced for the
first time, where real customers get to rate systems developed by leading
universities worldwide. The aim of the challenge is to converse "coherently and
engagingly with humans on popular topics for 20 minutes". We describe our Alexa
Prize system (called 'Alana') consisting of an ensemble of bots, combining
rule-based and machine learning systems, and using a contextual ranking
mechanism to choose a system response. The ranker was trained on real user
feedback received during the competition, where we address the problem of how
to train on the noisy and sparse feedback obtained during the competition.Comment: NIPS 2017 Workshop on Conversational A
A Crowd-based Evaluation of Abuse Response Strategies in Conversational Agents
How should conversational agents respond to verbal abuse through the user? To
answer this question, we conduct a large-scale crowd-sourced evaluation of
abuse response strategies employed by current state-of-the-art systems. Our
results show that some strategies, such as "polite refusal" score highly across
the board, while for other strategies demographic factors, such as age, as well
as the severity of the preceding abuse influence the user's perception of which
response is appropriate. In addition, we find that most data-driven models lag
behind rule-based or commercial systems in terms of their perceived
appropriateness