Dialogue Act (DA) classification is the task of classifying utterances with respect to the function they serve in a dialogue. Existing approaches to DA classification model utterances without incorporating the turn changes among speakers throughout t
he dialogue, therefore treating it no different than non-interactive written text. In this paper, we propose to integrate the turn changes in conversations among speakers when modeling DAs. Specifically, we learn conversation-invariant speaker turn embeddings to represent the speaker turns in a conversation; the learned speaker turn embeddings are then merged with the utterance embeddings for the downstream task of DA classification. With this simple yet effective mechanism, our model is able to capture the semantics from the dialogue content while accounting for different speaker turns in a conversation. Validation on three benchmark public datasets demonstrates superior performance of our model.
Using a corpus of compiled codes from U.S. states containing labeled tax law sections, we train text classifiers to automatically tag tax-law documents and, further, to identify the associated revenue source (e.g. income, property, or sales). After e
valuating classifier performance in held-out test data, we apply them to an historical corpus of U.S. state legislation to extract the flow of relevant laws over the years 1910 through 2010. We document that the classifiers are effective in the historical corpus, for example by automatically detecting establishments of state personal income taxes. The trained models with replication code are published at https://github.com/luyang521/tax-classification.
This study proposes an utterance position-aware approach for a neural network-based dialogue act recognition (DAR) model, which incorporates positional encoding for utterance's absolute or relative position. The proposed approach is inspired by the o
bservation that some dialogue acts have tendencies of occurrence positions. The evaluations on the Switchboard corpus show that the proposed positional encoding of utterances statistically significantly improves the performance of DAR.
Information extraction and question answering have the potential to introduce a new paradigm for how machine learning is applied to criminal law. Existing approaches generally use tabular data for predictive metrics. An alternative approach is needed
for matters of equitable justice, where individuals are judged on a case-by-case basis, in a process involving verbal or written discussion and interpretation of case factors. Such discussions are individualized, but they nonetheless rely on underlying facts. Information extraction can play an important role in surfacing these facts, which are still important to understand. We analyze unsupervised, weakly supervised, and pre-trained models' ability to extract such factual information from the free-form dialogue of California parole hearings. With a few exceptions, most F1 scores are below 0.85. We use this opportunity to highlight some opportunities for further research for information extraction and question answering. We encourage new developments in NLP to enable analysis and review of legal cases to be done in a post-hoc, not predictive, manner.
This article explores the potential for Natural Language Processing (NLP) to enable a more effective, prevention focused and less confrontational policing model that has hitherto been too resource consuming to implement at scale. Problem-Oriented Pol
icing (POP) is a potential replacement, at least in part, for traditional policing which adopts a reactive approach, relying heavily on the criminal justice system. By contrast, POP seeks to prevent crime by manipulating the underlying conditions that allow crimes to be committed. Identifying these underlying conditions requires a detailed understanding of crime events - tacit knowledge that is often held by police officers but which can be challenging to derive from structured police data. One potential source of insight exists in unstructured free text data commonly collected by police for the purposes of investigation or administration. Yet police agencies do not typically have the skills or resources to analyse these data at scale. In this article we argue that NLP offers the potential to unlock these unstructured data and by doing so allow police to implement more POP initiatives. However we caution that using NLP models without adequate knowledge may either allow or perpetuate bias within the data potentially leading to unfavourable outcomes.
Alzheimer's Disease (AD) is associated with many characteristic changes, not only in an individual's language but also in the interactive patterns observed in dialogue. The most indicative changes of this latter kind tend to be associated with relati
vely rare dialogue acts (DAs), such as those involved in clarification exchanges and responses to particular kinds of questions. However, most existing work in DA tagging focuses on improving average performance, effectively prioritizing more frequent classes; it thus gives a poor performance on these rarer classes and is not suited for application to AD analysis. In this paper, we investigate tagging specifically for rare class DAs, using a hierarchical BiLSTM model with various ways of incorporating information from previous utterances and DA tags in context. We show that this can give good performance for rare DA classes on both the general Switchboard corpus (SwDA) and an AD-specific conversational dataset, the Carolinas Conversation Collection (CCC); and that the tagger outputs then contribute useful information for distinguishing patients with and without AD
The importance of proof of nationality derives from the importance of nationality itself and in terms of it being the adaptation of the individual’s life in the state and in the international community. The individual’s need to prove his nationality
is related to his daily life as much of his rights, obligations, actions, and relations with others depend on it. The importance of proving nationality does not seem necessary only when a judicial dispute over a person’s nationality arises, but it also takes place outside the framework of the judicial dispute and in every case in which it is necessary to prove the person’s nationality status, whether to defend his interests or confront others or to determine his treatment in terms of rights and obligations With regard to the various authorities and authorities in the concerned countries or before other countries.
We use dialogue act recognition (DAR) to investigate how well BERT represents utterances in dialogue, and how fine-tuning and large-scale pre-training contribute to its performance. We find that while both the standard BERT pre-training and pretraini
ng on dialogue-like data are useful, task-specific fine-tuning is essential for good performance.
This research aims to find the necessary conditions for the existence of the dark soliton solution to the Vakhnenko-Parkes equation with time dependent coefficients and with power law nonlinearity by using the solitary wave ansatz method. The value o
f the power law nonlinearity parameter is determined. The results show that the used method is efficient to obtain this kind of solutions for the nonlinear partial differential equations.
The aim of this research is to evaluate the rights of taxpayers in the Syrian tax legislation, especially the Income Tax Law No. (24) for the year 2003 and its amendments, and related laws. The evaluation process includes the availability of these ri
ghts from a legislative point of view, on the other hand, to ensure the extent of their application in practice by the tax administration. A set of fundamental rights have been identified and agreed to by most tax systems, in addition to the statement of the Organization for Economic Cooperation and Development that has been adopted as a reference document in this field. These rights are the right to information, assistance, and listening, the right to appeal, the right to pay no more than the correct amount of tax, the right to certainty, the right to privacy, the right to confidentiality and confidentiality. The descriptive approach was mainly used in constructing the problem of this research and developing its hypotheses. In addition to using research tools such as personal interviews that included many employees in the tax administration, and access to some practical cases in that administration, in addition to the questionnaire that included a sample of taxpayer income. The data collected was analyzed using the SPSS statistical program and the Likert binary scale. The results of this research have shown that the Income Tax Law No. 24 of 2003 and other related laws have not explicitly, clearly and completely stipulated most of these rights, and their non-application by the tax administration. These rights are the right of the taxpayer to obtain information, assistance and listening, the right to certainty, the right of the taxpayer to pay no more than the correct amount of taxes, and the right to privacy. It also showed the important imbalance in the right to object, and stipulated the right to confidentiality only.
