Matrix Factorization with Knowledge Graph Propagation for Unsupervised Spoken Language Understanding

Spoken dialogue systems (SDS) typically require a predefined semantic ontology to train a spoken language understanding (SLU) module. In addition to the anno-tation cost, a key challenge for design-ing such an ontology is to define a coher-ent slot set while considering their com-plex relations. This paper introduces a novel matrix factorization (MF) approach to learn latent feature vectors for utter-ances and semantic elements without the need of corpus annotations. Specifically, our model learns the semantic slots for a domain-specific SDS in an unsupervised fashion, and carries out semantic pars-ing using latent MF techniques. To fur-ther consider the global semantic struc-ture, such as inter-word and inter-slot re-lations, we augment the latent MF-based model with a knowledge graph propaga-tion model based on a slot-based seman-tic graph and a word-based lexical graph. Our experiments show that the proposed MF approaches produce better SLU mod-els that are able to predict semantic slots and word patterns taking into account their relations and domain-specificity in a joint manner.

DERIVING LOCAL RELATIONAL SURFACE FORMS FROM DEPENDENCY-BASED ENTITY EMBEDDINGS FOR UNSUPERVISED SPOKEN LANGUAGE UNDERSTANDING

ABSTRACT Recent works showed the trend of leveraging web-scaled structured semantic knowledge resources such as Freebase for open domain spoken language understanding (SLU). Knowledge graphs provide sufficient but ambiguous relations for the same entity, which can be used as statistical background knowledge to infer possible relations for interpretation of user utterances. This paper proposes an approach to capture the relational surface forms by mapping dependency-based contexts of entities from the text domain to the spoken domain. Relational surface forms are learned from dependency-based entity embeddings, which encode the contexts of entities from dependency trees in a deep learning model. The derived surface forms carry functional dependency to the entities and convey the explicit expression of relations. The experiments demonstrate the efficiency of leveraging derived relational surface forms as local cues together with prior background knowledge. Index Terms-spoken language understanding (SLU), relation detection, semantic knowledge graph, entity embeddings, spoken dialogue systems (SDS)

LogBERT: Log Anomaly Detection via BERT

When systems break down, administrators usually check the produced logs to diagnose the failures. Nowadays, systems grow larger and more complicated. It is labor-intensive to manually detect abnormal behaviors in logs. Therefore, it is necessary to develop an automated anomaly detection on system logs. Automated anomaly detection not only identifies malicious patterns promptly but also requires no prior domain knowledge. Many existing log anomaly detection approaches apply natural language models such as Recurrent Neural Network (RNN) to log analysis since both are based on sequential data. The proposed model, LogBERT, a BERT-based neural network, can capture the contextual information in log sequences. LogBERT is trained on normal log data considering the scarcity of labeled abnormal data in reality. Intuitively, LogBERT learns normal patterns in training data and flags test data that are deviated from prediction as anomalies. We compare LogBERT with four traditional machine learning models and two deep learning models in terms of precision, recall, and F1 score on three public datasets, HDFS, BGL, and Thunderbird. Overall, LogBERT outperforms the state-of-art models for log anomaly detection

Towards Query Logs for Privacy Studies: On Deriving Search Queries from Questions

Translating verbose information needs into crisp search queries is a phenomenon that is ubiquitous but hardly understood. Insights into this process could be valuable in several applications, including synthesizing large privacy-friendly query logs from public Web sources which are readily available to the academic research community. In this work, we take a step towards understanding query formulation by tapping into the rich potential of community question answering (CQA) forums. Specifically, we sample natural language (NL) questions spanning diverse themes from the Stack Exchange platform, and conduct a large-scale conversion experiment where crowdworkers submit search queries they would use when looking for equivalent information. We provide a careful analysis of this data, accounting for possible sources of bias during conversion, along with insights into user-specific linguistic patterns and search behaviors. We release a dataset of 7,000 question-query pairs from this study to facilitate further research on query understanding.Comment: ECIR 2020 Short Pape