Intro; Preface; Organization; Contents; Invited Talk; The Time-Course of Phoneme Category Adaptation in Deep Neural Networks; Abstract; 1 Introduction; 2 Methods; 2.1 Model Architecture; 2.2 Retuning Conditions; 3 Classification Rates; 4 Discussion and Concluding Remarks; Acknowledgements; References; Dialogue and Spoken Language Understanding; Towards Pragmatic Understanding of Conversational Intent: A Multimodal Annotation Approach to Multiparty Informal Interaction
The EVA Corpus; Abstract; 1 Introduction; 2 Background; 3 Data Collection and Methodology: The EVA Corpus; 3.1 Data Source

3.2 Annotation Topology3.3 Annotation Procedure and Inter-annotator Agreement; 3.4 Transcription and Segmentation; 3.5 Discourse Management and Structuring; 3.6 Discourse Markers; 3.7 Emotion; 3.8 Classification of Embodied Behavior Through Semiotic Intent; 3.9 Form and Structure of Non-verbal Expressions; 4 Conclusion; Acknowledgments; References; Lilia, A Showcase for Fast Bootstrap of Conversation-Like Dialogues Based on a Goal-Oriented System; 1 Introduction; 2 Comparison of Goal-Oriented Vs Conversational Agents; 3 Conversion from Goal-Oriented to Conversational; 4 Evaluation

5 ConclusionReferences; Recent Advances in End-to-End Spoken Language Understanding; 1 Introduction; 2 SLU Tasks; 3 Model Training; 3.1 CTC Loss Function Interpretation Related to -mode; 3.2 Speaker Adaptive Training; 3.3 Transfer Learning; 4 Experiments; 4.1 Data; 4.2 Models; 4.3 Tasks; 4.4 Results for NER; 4.5 Results for SF; 5 Conclusions; References; Language Analysis and Generation; A Study on Multilingual Transfer Learning in Neural Machine Translation: Finding the Balance Between Languages; 1 Introduction; 2 Related Work; 3 Data; 3.1 Data Selection; 3.2 Data Preprocessing

3.3 SPM Model Study4 Architecture; 5 Experiments; 5.1 Results; 6 Conclusion; References; A Deep Learning Approach to Self-expansion of Abbreviations Based on Morphology and Context Distance; 1 Introduction; 2 Related Works; 3 Our Method; 3.1 Step 1: Abbreviation Identification; 3.2 Step 2: Full Form Candidates Identification; 3.3 Step 3: Abbreviation Disambiguation; 4 Evaluation; 4.1 Abbreviation Identification (Step 1); 4.2 Full Form Candidates Identification (Step 2); 4.3 Abbreviation Disambiguation (Step 3); 5 Conclusion; References; Word Sense Induction Using Word Sketches

1 Word Sense Induction2 Spectral Clustering; 3 Clustering of Word Sketch Co-occurrences; 3.1 Clustering of Word Sketch Thesaurus; 3.2 Clustering Context Word Embeddings; 3.3 Clustering Word Sketches by Word Embeddings; 4 Conclusion; References; Temporal ``Declination'' in Different Types of IPs in Russian: Preliminary Results; 1 Introduction; 2 Materials and Methods; 3 Results and Discussion; 4 Conclusions; References; Geometry and Analogies: A Study and Propagation Method for Word Representations; 1 Introduction; 1.1 Context and Motivations; 1.2 Contributions; 2 Related Work