986 research outputs found
Dialogue Act Recognition via CRF-Attentive Structured Network
Dialogue Act Recognition (DAR) is a challenging problem in dialogue
interpretation, which aims to attach semantic labels to utterances and
characterize the speaker's intention. Currently, many existing approaches
formulate the DAR problem ranging from multi-classification to structured
prediction, which suffer from handcrafted feature extensions and attentive
contextual structural dependencies. In this paper, we consider the problem of
DAR from the viewpoint of extending richer Conditional Random Field (CRF)
structural dependencies without abandoning end-to-end training. We incorporate
hierarchical semantic inference with memory mechanism on the utterance
modeling. We then extend structured attention network to the linear-chain
conditional random field layer which takes into account both contextual
utterances and corresponding dialogue acts. The extensive experiments on two
major benchmark datasets Switchboard Dialogue Act (SWDA) and Meeting Recorder
Dialogue Act (MRDA) datasets show that our method achieves better performance
than other state-of-the-art solutions to the problem. It is a remarkable fact
that our method is nearly close to the human annotator's performance on SWDA
within 2% gap.Comment: 10 pages, 4figure
Thread Reconstruction in Conversational Data using Neural Coherence Models
Discussion forums are an important source of information. They are often used
to answer specific questions a user might have and to discover more about a
topic of interest. Discussions in these forums may evolve in intricate ways,
making it difficult for users to follow the flow of ideas. We propose a novel
approach for automatically identifying the underlying thread structure of a
forum discussion. Our approach is based on a neural model that computes
coherence scores of possible reconstructions and then selects the highest
scoring, i.e., the most coherent one. Preliminary experiments demonstrate
promising results outperforming a number of strong baseline methods.Comment: Neu-IR: Workshop on Neural Information Retrieval 201
Conversation Trees: A Grammar Model for Topic Structure in Forums
Online forum discussions proceed differently from face-to-face conversations and any single thread on an online forum contains posts on different subtopics. This work aims to characterize the content of a forum thread as a conversation tree of topics. We present models that jointly per- form two tasks: segment a thread into sub- parts, and assign a topic to each part. Our core idea is a definition of topic structure using probabilistic grammars. By leveraging the flexibility of two grammar formalisms, Context-Free Grammars and Linear Context-Free Rewriting Systems, our models create desirable structures for forum threads: our topic segmentation is hierarchical, links non-adjacent segments on the same topic, and jointly labels the topic during segmentation. We show that our models outperform a number of tree generation baselines
Towards Feasible Instructor Intervention in MOOC discussion forums
Massive Open Online Courses allow numerous people from around the world to have access to knowledge that they otherwise have not. However, high student-to-instructor ratio in MOOCs restricts instructors’ ability to facilitate student learning by intervening in discussions forums, as they do in face-to-face classrooms. Instructors need automated guidance on when and how to intervene in discussion forums. Using a typology of pedagogical interventions derived from prior research, we annotate a large corpus of discussion forum contents to enable supervised machine learning to automatically identify interventions that promote student learning. Such machine learning models may allow building of dashboards to automatically prompt instructors on when and how to intervene in discussion forums. In the longer term, it may be possible to automate these interventions relieving instructors of this effort. Such automated approaches are essential for allowing good pedagogical practices to scale in the context of MOOC discussion forums
Topic Segmentation of Semi-Structured and Unstructured Conversational Datasets using Language Models
Breaking down a document or a conversation into multiple contiguous segments
based on its semantic structure is an important and challenging problem in NLP,
which can assist many downstream tasks. However, current works on topic
segmentation often focus on segmentation of structured texts. In this paper, we
comprehensively analyze the generalization capabilities of state-of-the-art
topic segmentation models on unstructured texts. We find that: (a) Current
strategies of pre-training on a large corpus of structured text such as
Wiki-727K do not help in transferability to unstructured conversational data.
(b) Training from scratch with only a relatively small-sized dataset of the
target unstructured domain improves the segmentation results by a significant
margin. We stress-test our proposed Topic Segmentation approach by
experimenting with multiple loss functions, in order to mitigate effects of
imbalance in unstructured conversational datasets. Our empirical evaluation
indicates that Focal Loss function is a robust alternative to Cross-Entropy and
re-weighted Cross-Entropy loss function when segmenting unstructured and
semi-structured chats.Comment: Accepted to IntelliSys 2023. arXiv admin note: substantial text
overlap with arXiv:2211.1495
Topic-Aware Multi-turn Dialogue Modeling
In the retrieval-based multi-turn dialogue modeling, it remains a challenge
to select the most appropriate response according to extracting salient
features in context utterances. As a conversation goes on, topic shift at
discourse-level naturally happens through the continuous multi-turn dialogue
context. However, all known retrieval-based systems are satisfied with
exploiting local topic words for context utterance representation but fail to
capture such essential global topic-aware clues at discourse-level. Instead of
taking topic-agnostic n-gram utterance as processing unit for matching purpose
in existing systems, this paper presents a novel topic-aware solution for
multi-turn dialogue modeling, which segments and extracts topic-aware
utterances in an unsupervised way, so that the resulted model is capable of
capturing salient topic shift at discourse-level in need and thus effectively
track topic flow during multi-turn conversation. Our topic-aware modeling is
implemented by a newly proposed unsupervised topic-aware segmentation algorithm
and Topic-Aware Dual-attention Matching (TADAM) Network, which matches each
topic segment with the response in a dual cross-attention way. Experimental
results on three public datasets show TADAM can outperform the state-of-the-art
method, especially by 3.3% on E-commerce dataset that has an obvious topic
shift
Aplicação de técnicas de Clustering ao contexto da Tomada de Decisão em Grupo
Nowadays, decisions made by executives and managers are primarily made in a group. Therefore, group decision-making is a process where a group of people called participants work together to analyze a set of variables, considering and evaluating a set of alternatives to select one or more solutions. There are many problems associated with group decision-making, namely when the participants cannot meet for any reason, ranging from schedule incompatibility to being in different countries with different time zones. To support this process, Group Decision Support Systems (GDSS) evolved to what today we call web-based GDSS. In GDSS, argumentation is ideal since it makes it easier to use justifications and explanations in interactions between decision-makers so they can sustain their opinions. Aspect Based Sentiment Analysis (ABSA) is a subfield of Argument Mining closely related to Natural Language Processing. It intends to classify opinions at the aspect level and identify the elements of an opinion. Applying ABSA techniques to Group Decision Making Context results in the automatic identification of alternatives and criteria, for example. This automatic identification is essential to reduce the time decision-makers take to step themselves up on Group Decision Support Systems and offer them various insights and knowledge on the discussion they are participants. One of these insights can be arguments getting used by the decision-makers about an alternative. Therefore, this dissertation proposes a methodology that uses an unsupervised technique, Clustering, and aims to segment the participants of a discussion based on arguments used so it can produce knowledge from the current information in the GDSS. This methodology can be hosted in a web service that follows a micro-service architecture and utilizes Data Preprocessing and Intra-sentence Segmentation in addition to Clustering to achieve the objectives of the dissertation. Word Embedding is needed when we apply clustering techniques to natural language text to transform the natural language text into vectors usable by the clustering techniques. In addition to Word Embedding, Dimensionality Reduction techniques were tested to improve the results. Maintaining the same Preprocessing steps and varying the chosen Clustering techniques, Word Embedders, and Dimensionality Reduction techniques came up with the best approach. This approach consisted of the KMeans++ clustering technique, using SBERT as the word embedder with UMAP dimensionality reduction, reducing the number of dimensions to 2. This experiment achieved a Silhouette Score of 0.63 with 8 clusters on the baseball dataset, which wielded good cluster results based on their manual review and Wordclouds. The same approach obtained a Silhouette Score of 0.59 with 16 clusters on the car brand dataset, which we used as an approach validation dataset.Atualmente, as decisões tomadas por gestores e executivos são maioritariamente realizadas em grupo. Sendo assim, a tomada de decisão em grupo é um processo no qual um grupo de pessoas denominadas de participantes, atuam em conjunto, analisando um conjunto de variáveis, considerando e avaliando um conjunto de alternativas com o objetivo de selecionar uma ou mais soluções. Existem muitos problemas associados ao processo de tomada de decisão, principalmente quando os participantes não têm possibilidades de se reunirem (Exs.: Os participantes encontramse em diferentes locais, os países onde estão têm fusos horários diferentes, incompatibilidades de agenda, etc.). Para suportar este processo de tomada de decisão, os Sistemas de Apoio à Tomada de Decisão em Grupo (SADG) evoluíram para o que hoje se chamam de Sistemas de Apoio à Tomada de Decisão em Grupo baseados na Web. Num SADG, argumentação é ideal pois facilita a utilização de justificações e explicações nas interações entre decisores para que possam suster as suas opiniões. Aspect Based Sentiment Analysis (ABSA) é uma área de Argument Mining correlacionada com o Processamento de Linguagem Natural. Esta área pretende classificar opiniões ao nível do aspeto da frase e identificar os elementos de uma opinião. Aplicando técnicas de ABSA à Tomada de Decisão em Grupo resulta na identificação automática de alternativas e critérios por exemplo. Esta identificação automática é essencial para reduzir o tempo que os decisores gastam a customizarem-se no SADG e oferece aos mesmos conhecimento e entendimentos sobre a discussão ao qual participam. Um destes entendimentos pode ser os argumentos a serem usados pelos decisores sobre uma alternativa. Assim, esta dissertação propõe uma metodologia que utiliza uma técnica não-supervisionada, Clustering, com o objetivo de segmentar os participantes de uma discussão com base nos argumentos usados pelos mesmos de modo a produzir conhecimento com a informação atual no SADG. Esta metodologia pode ser colocada num serviço web que segue a arquitetura micro serviços e utiliza Preprocessamento de Dados e Segmentação Intra Frase em conjunto com o Clustering para atingir os objetivos desta dissertação. Word Embedding também é necessário para aplicar técnicas de Clustering a texto em linguagem natural para transformar o texto em vetores que possam ser usados pelas técnicas de Clustering. Também Técnicas de Redução de Dimensionalidade também foram testadas de modo a melhorar os resultados. Mantendo os passos de Preprocessamento e variando as técnicas de Clustering, Word Embedder e as técnicas de Redução de Dimensionalidade de modo a encontrar a melhor abordagem. Essa abordagem consiste na utilização da técnica de Clustering KMeans++ com o SBERT como Word Embedder e UMAP como a técnica de redução de dimensionalidade, reduzindo as dimensões iniciais para duas. Esta experiência obteve um Silhouette Score de 0.63 com 8 clusters no dataset de baseball, que resultou em bons resultados de cluster com base na sua revisão manual e visualização dos WordClouds. A mesma abordagem obteve um Silhouette Score de 0.59 com 16 clusters no dataset das marcas de carros, ao qual usamos esse dataset com validação de abordagem
Learning functional sections in medical conversations: iterative pseudo-labeling and human-in-the-loop approach
Medical conversations between patients and medical professionals have
implicit functional sections, such as "history taking", "summarization",
"education", and "care plan." In this work, we are interested in learning to
automatically extract these sections. A direct approach would require
collecting large amounts of expert annotations for this task, which is
inherently costly due to the contextual inter-and-intra variability between
these sections. This paper presents an approach that tackles the problem of
learning to classify medical dialogue into functional sections without
requiring a large number of annotations. Our approach combines pseudo-labeling
and human-in-the-loop. First, we bootstrap using weak supervision with
pseudo-labeling to generate dialogue turn-level pseudo-labels and train a
transformer-based model, which is then applied to individual sentences to
create noisy sentence-level labels. Second, we iteratively refine
sentence-level labels using a cluster-based human-in-the-loop approach. Each
iteration requires only a few dozen annotator decisions. We evaluate the
results on an expert-annotated dataset of 100 dialogues and find that while our
models start with 69.5% accuracy, we can iteratively improve it to 82.5%. The
code used to perform all experiments described in this paper can be found here:
https://github.com/curai/curai-research/tree/main/functional-sections.Comment: Changed the github link as it was invali
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