Search Results for author:
Found 12 papers, 3 papers with code
Service registration chatbot: collecting and comparing dialogues from AMT workers and service’s users
In this article, we compare two crowdsourcing sources on a dialogue paraphrasing task revolving around a chatbot service.
End-to-end Ensemble-based Feature Selection for Paralinguistics Tasks
The current state-of-the-art methods proposed for these tasks are ensembles based on deep neural networks like ResNets in conjunction with feature engineering.
Data augmentation using prosody and false starts to recognize non-native children's speech
Firstly, we apply the prosody-based data augmentation to supplement the audio data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
FinChat: Corpus and evaluation setup for Finnish chat conversations on everyday topics
Using this corpus, we also construct a retrieval-based evaluation task for Finnish chatbot development.
Aalto's End-to-End DNN systems for the INTERSPEECH 2020 Computational Paralinguistics Challenge
On ComParE 2020 tasks, we investigate applying an ensemble of E2E models for robust performance and developing task-specific modifications for each task.
Effects of Language Relatedness for Cross-lingual Transfer Learning in Character-Based Language Models
We, however, show that for character-based NNLMs, only pretraining with a related language improves the ASR performance, and using an unrelated language may deteriorate it.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Subword RNNLM Approximations for Out-Of-Vocabulary Keyword Search
On these tasks, interpolating the baseline RNNLM approximation and a conventional LM outperforms the conventional LM in terms of the Maximum Term Weighted Value for single-character subwords.
Handling Noisy Labels for Robustly Learning from Self-Training Data for Low-Resource Sequence Labeling
In our experiments on Chunking and NER, this approach performs more robustly than the baselines.
Long-Short Range Context Neural Networks for Language Modeling
The goal of language modeling techniques is to capture the statistical and structural properties of natural languages from training corpora.
Sequential Recurrent Neural Networks for Language Modeling
Feedforward Neural Network (FNN)-based language models estimate the probability of the next word based on the history of the last N words, whereas Recurrent Neural Networks (RNN) perform the same task based only on the last word and some context information that cycles in the network.
Sub-Word Similarity based Search for Embeddings: Inducing Rare-Word Embeddings for Word Similarity Tasks and Language Modelling
We augmented pre-trained word embeddings with these novel embeddings and evaluated on a rare word similarity task, obtaining up to 3 times improvement in correlation over the original set of embeddings.
Effective Slot Filling Based on Shallow Distant Supervision Methods
In the TAC KBP 2013 English Slotfilling evaluation, the submitted main run of the LSV RelationFactory system achieved the top-ranked F1-score of 37. 3%.
