Filling Conversation Ellipsis for Better Social Dialog Understanding

The phenomenon of ellipsis is prevalent in social conversations. Ellipsis increases the difficulty of a series of downstream language understanding tasks, such as dialog act prediction and semantic role labeling. We propose to resolve ellipsis through automatic sentence completion to improve language understanding. However, automatic ellipsis completion can result in output which does not accurately reflect user intent. To address this issue, we propose a method which considers both the original utterance that has ellipsis and the automatically completed utterance in dialog act and semantic role labeling tasks. Specifically, we first complete user utterances to resolve ellipsis using an end-to-end pointer network model. We then train a prediction model using both utterances containing ellipsis and our automatically completed utterances. Finally, we combine the prediction results from these two utterances using a selection model that is guided by expert knowledge. Our approach improves dialog act prediction and semantic role labeling by 1.3% and 2.5% in F1 score respectively in social conversations. We also present an open-domain human-machine conversation dataset with manually completed user utterances and annotated semantic role labeling after manual completion.Comment: Accepted to AAAI 202

Combination Strategies for Semantic Role Labeling

This paper introduces and analyzes a battery of inference models for the problem of semantic role labeling: one based on constraint satisfaction, and several strategies that model the inference as a meta-learning problem using discriminative classifiers. These classifiers are developed with a rich set of novel features that encode proposition and sentence-level information. To our knowledge, this is the first work that: (a) performs a thorough analysis of learning-based inference models for semantic role labeling, and (b) compares several inference strategies in this context. We evaluate the proposed inference strategies in the framework of the CoNLL-2005 shared task using only automatically-generated syntactic information. The extensive experimental evaluation and analysis indicates that all the proposed inference strategies are successful -they all outperform the current best results reported in the CoNLL-2005 evaluation exercise- but each of the proposed approaches has its advantages and disadvantages. Several important traits of a state-of-the-art SRL combination strategy emerge from this analysis: (i) individual models should be combined at the granularity of candidate arguments rather than at the granularity of complete solutions; (ii) the best combination strategy uses an inference model based in learning; and (iii) the learning-based inference benefits from max-margin classifiers and global feedback

Semantic analysis for paraphrase identification using semantic role labeling

Reuse of documents has been prominently appeared during the course of digitalization of information contents owing to the wide-spread of internet and smartphones in various complex forms such as inserting words, omitting and substituting, changing word order, and etc. Especially, when a word in document is substituted with a similar word, it would be an issue not to consider it as a subject of measurement for the existing morphological similarity measurement method. In order to resolve this kind of problem, various researches have been conducted on the similarity measurement considering semantic information. This study is to propose a measurement method on semantic similarity being characterized as semantic role information in sentences acquired by semantic role labeling. To assess the performance of this proposed method, it was compared with the method of substring similarity being utilized for similarity measurement for existing documents. As a result, we could identify that the proposed method performed similar with the conventional method for the plagiarized documents which were rarely modified whereas it had improved results for paraphrasing sentences which were changed in structure

Abstractive Multi-Document Summarization based on Semantic Link Network

The key to realize advanced document summarization is semantic representation of documents. This paper investigates the role of Semantic Link Network in representing and understanding documents for multi-document summarization. It proposes a novel abstractive multi-document summarization framework by first transforming documents into a Semantic Link Network of concepts and events and then transforming the Semantic Link Network into the summary of the documents based on the selection of important concepts and events while keeping semantics coherence. Experiments on benchmark datasets show that the proposed summarization approach significantly outperforms relevant state-of-the-art baselines and the Semantic Link Network plays an important role in representing and understanding documents

Four Lessons in Versatility or How Query Languages Adapt to the Web

Exposing not only human-centered information, but machine-processable data on the Web is one of the commonalities of recent Web trends. It has enabled a new kind of applications and businesses where the data is used in ways not foreseen by the data providers. Yet this exposition has fractured the Web into islands of data, each in different Web formats: Some providers choose XML, others RDF, again others JSON or OWL, for their data, even in similar domains. This fracturing stifles innovation as application builders have to cope not only with one Web stack (e.g., XML technology) but with several ones, each of considerable complexity. With Xcerpt we have developed a rule- and pattern based query language that aims to give shield application builders from much of this complexity: In a single query language XML and RDF data can be accessed, processed, combined, and re-published. Though the need for combined access to XML and RDF data has been recognized in previous work (including the W3C’s GRDDL), our approach differs in four main aspects: (1) We provide a single language (rather than two separate or embedded languages), thus minimizing the conceptual overhead of dealing with disparate data formats. (2) Both the declarative (logic-based) and the operational semantics are unified in that they apply for querying XML and RDF in the same way. (3) We show that the resulting query language can be implemented reusing traditional database technology, if desirable. Nevertheless, we also give a unified evaluation approach based on interval labelings of graphs that is at least as fast as existing approaches for tree-shaped XML data, yet provides linear time and space querying also for many RDF graphs. We believe that Web query languages are the right tool for declarative data access in Web applications and that Xcerpt is a significant step towards a more convenient, yet highly efficient data access in a “Web of Data”

Utilizing External Knowledge to Enhance Semantics in Emotion Detection

Enabling machines to emotion recognition in conversation is challenging, mainly because the information in human dialogue innately conveys emotions by long-term experience, abundant knowledge, context, and the intricate patterns between the affective states. We address the task of emotion recognition in conversations using external knowledge to enhance semantics. We propose KES model, a new framework that incorporates different elements of external knowledge and conversational semantic role labeling, where build upon them to learn interactions between interlocutors participating in a conversation. We design a self-attention layer specialized for enhanced semantic text features with external commonsense knowledge. Then, two different networks composed of LSTM are responsible for tracking individual internal state and context external state. In addition, the proposed model has experimented on three datasets in emotion detection in conversation. The experimental results show that our model outperforms the state-of-the-art approaches on most of the tested datasets

Rewriting-based Verification and Debugging of Web Systems

The increasing complexity of Web system has led to the development of sophisticated formal methodologies for verifying and correcting Web data and Web programs. In general, establishing whether a Web system behaves correctly with respect to the original intention of the programmer or checking its internal consistency are non-trivial tasks as witnessed by many studies in the literature. In this dissertation, we face two challenging problems related to the verification of Web systems. Firstly, we extend a previous Web verification framework based on partial rewriting by providing a semi-automatic technique for repairing Web systems. We propose a basic repairing methodology that is endowed with several strategies for optimizing the number of repair actions that must be executed in order to fix a given Web site. Also, we develop an improvement of the Web verification framework that is based on abstract interpretation and greatly enhances both efficiency and scalability of the original technique. Secondly, we formalize a framework for the specification and model-checking of dynamic Web applications that is based on Rewriting Logic. Our framework allows one to simulate the user navigation and the evaluation of Web scripts within a Web application, and also check important related properties such us reachability and consistency. When a property is refuted, a counter-example with the erroneous trace is delivered. Such information can be analyzed in order to debug the Web application under examination by means of a novel backward trace slicing technique that we formulated for this purpose. This technique consists in tracing back, along an execution trace, all the relevant symbols of the term (or state) that we are interested to observe.Romero ., DO. (2011). Rewriting-based Verification and Debugging of Web Systems [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/12496Palanci