50,698 research outputs found
Abusive Language Detection in Online Conversations by Combining Content-and Graph-based Features
In recent years, online social networks have allowed worldwide users to meet
and discuss. As guarantors of these communities, the administrators of these
platforms must prevent users from adopting inappropriate behaviors. This
verification task, mainly done by humans, is more and more difficult due to the
ever growing amount of messages to check. Methods have been proposed to
automatize this moderation process, mainly by providing approaches based on the
textual content of the exchanged messages. Recent work has also shown that
characteristics derived from the structure of conversations, in the form of
conversational graphs, can help detecting these abusive messages. In this
paper, we propose to take advantage of both sources of information by proposing
fusion methods integrating content-and graph-based features. Our experiments on
raw chat logs show that the content of the messages, but also of their dynamics
within a conversation contain partially complementary information, allowing
performance improvements on an abusive message classification task with a final
F-measure of 93.26%
"How May I Help You?": Modeling Twitter Customer Service Conversations Using Fine-Grained Dialogue Acts
Given the increasing popularity of customer service dialogue on Twitter,
analysis of conversation data is essential to understand trends in customer and
agent behavior for the purpose of automating customer service interactions. In
this work, we develop a novel taxonomy of fine-grained "dialogue acts"
frequently observed in customer service, showcasing acts that are more suited
to the domain than the more generic existing taxonomies. Using a sequential
SVM-HMM model, we model conversation flow, predicting the dialogue act of a
given turn in real-time. We characterize differences between customer and agent
behavior in Twitter customer service conversations, and investigate the effect
of testing our system on different customer service industries. Finally, we use
a data-driven approach to predict important conversation outcomes: customer
satisfaction, customer frustration, and overall problem resolution. We show
that the type and location of certain dialogue acts in a conversation have a
significant effect on the probability of desirable and undesirable outcomes,
and present actionable rules based on our findings. The patterns and rules we
derive can be used as guidelines for outcome-driven automated customer service
platforms.Comment: 13 pages, 6 figures, IUI 201
User Intent Prediction in Information-seeking Conversations
Conversational assistants are being progressively adopted by the general
population. However, they are not capable of handling complicated
information-seeking tasks that involve multiple turns of information exchange.
Due to the limited communication bandwidth in conversational search, it is
important for conversational assistants to accurately detect and predict user
intent in information-seeking conversations. In this paper, we investigate two
aspects of user intent prediction in an information-seeking setting. First, we
extract features based on the content, structural, and sentiment
characteristics of a given utterance, and use classic machine learning methods
to perform user intent prediction. We then conduct an in-depth feature
importance analysis to identify key features in this prediction task. We find
that structural features contribute most to the prediction performance. Given
this finding, we construct neural classifiers to incorporate context
information and achieve better performance without feature engineering. Our
findings can provide insights into the important factors and effective methods
of user intent prediction in information-seeking conversations.Comment: Accepted to CHIIR 201
"Thank you for a lovely day!" Contrastive thanking in textbooks for teaching English and Spanish as foreign languages
Thanking, as other speech acts such as apologizing or requesting,can be performed in numerous contexts and, for their analysis, many crucial variables must be taken into consideration (eg. social distance, gender, age,etc.), which often are difficult to control. Besides these variables, speech acts are carried out in different situations, taking into account the culture in which they are performed. For example, thanking might be performed after alighting a bus in the UK, the USA or Australia, but this might not necessarily happen in Spain. The aim of the study on which this paper is based, in to explore thanking contrastively in British English and in Peninsular Spanish from a pragmatic viewpoint,by looking at specific independent variables: the context and situation in which this speech act is performed, the relationship between the interlocutors who perform it, which includes social power and distance, and the reason for expressing gratitude. For the purpose of this investigation, a corpus of 128 textbooks (64 for each language) for the learning and teaching of Spanish and English as foreign languages was used. It is important to note that, although these corpora are built on prefabricated dialogues and these can be regarded as abstractions of reality, the communicative situations found in the textbooks are aimed at depicting exchanges and linguistic patterns representing what naturally occurs in real conversations in both cultures
Conversational Sensing
Recent developments in sensing technologies, mobile devices and context-aware
user interfaces have made it possible to represent information fusion and
situational awareness as a conversational process among actors - human and
machine agents - at or near the tactical edges of a network. Motivated by use
cases in the domain of security, policing and emergency response, this paper
presents an approach to information collection, fusion and sense-making based
on the use of natural language (NL) and controlled natural language (CNL) to
support richer forms of human-machine interaction. The approach uses a
conversational protocol to facilitate a flow of collaborative messages from NL
to CNL and back again in support of interactions such as: turning eyewitness
reports from human observers into actionable information (from both trained and
untrained sources); fusing information from humans and physical sensors (with
associated quality metadata); and assisting human analysts to make the best use
of available sensing assets in an area of interest (governed by management and
security policies). CNL is used as a common formal knowledge representation for
both machine and human agents to support reasoning, semantic information fusion
and generation of rationale for inferences, in ways that remain transparent to
human users. Examples are provided of various alternative styles for user
feedback, including NL, CNL and graphical feedback. A pilot experiment with
human subjects shows that a prototype conversational agent is able to gather
usable CNL information from untrained human subjects
Query Resolution for Conversational Search with Limited Supervision
In this work we focus on multi-turn passage retrieval as a crucial component
of conversational search. One of the key challenges in multi-turn passage
retrieval comes from the fact that the current turn query is often
underspecified due to zero anaphora, topic change, or topic return. Context
from the conversational history can be used to arrive at a better expression of
the current turn query, defined as the task of query resolution. In this paper,
we model the query resolution task as a binary term classification problem: for
each term appearing in the previous turns of the conversation decide whether to
add it to the current turn query or not. We propose QuReTeC (Query Resolution
by Term Classification), a neural query resolution model based on bidirectional
transformers. We propose a distant supervision method to automatically generate
training data by using query-passage relevance labels. Such labels are often
readily available in a collection either as human annotations or inferred from
user interactions. We show that QuReTeC outperforms state-of-the-art models,
and furthermore, that our distant supervision method can be used to
substantially reduce the amount of human-curated data required to train
QuReTeC. We incorporate QuReTeC in a multi-turn, multi-stage passage retrieval
architecture and demonstrate its effectiveness on the TREC CAsT dataset.Comment: SIGIR 2020 full conference pape
An Analysis of Mixed Initiative and Collaboration in Information-Seeking Dialogues
The ability to engage in mixed-initiative interaction is one of the core
requirements for a conversational search system. How to achieve this is poorly
understood. We propose a set of unsupervised metrics, termed ConversationShape,
that highlights the role each of the conversation participants plays by
comparing the distribution of vocabulary and utterance types. Using
ConversationShape as a lens, we take a closer look at several conversational
search datasets and compare them with other dialogue datasets to better
understand the types of dialogue interaction they represent, either driven by
the information seeker or the assistant. We discover that deviations from the
ConversationShape of a human-human dialogue of the same type is predictive of
the quality of a human-machine dialogue.Comment: SIGIR 2020 short conference pape
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