Search Results for author:
Found 22 papers, 4 papers with code
A Directional Diffusion Algorithm for Inpainting
The problem of inpainting involves reconstructing the missing areas of an image.
Leveraging Large Amounts of Weakly Supervised Data for Multi-Language Sentiment Classification
This paper presents a novel approach for multi-lingual sentiment classification in short texts.
TopicThunder at SemEval-2017 Task 4: Sentiment Classification Using a Convolutional Neural Network with Distant Supervision
In this paper, we propose a classifier for predicting topic-specific sentiments of English Twitter messages.
Twist Bytes - German Dialect Identification with Data Mining Optimization
We describe our approaches used in the German Dialect Identification (GDI) task at the VarDial Evaluation Campaign 2018.
Survey on Evaluation Methods for Dialogue Systems
We cover each class by introducing the main technologies developed for the dialogue systems and then by presenting the evaluation methods regarding this class.
Towards a Metric for Automated Conversational Dialogue System Evaluation and Improvement
We present "AutoJudge", an automated evaluation method for conversational dialogue systems.
Open-Ended Question Answering
Reinforcement Learning (RL)
+1
A Methodology for Creating Question Answering Corpora Using Inverse Data Annotation
For this, we introduce an intermediate representation that is based on the logical query plan in a database called Operation Trees (OT).
DoQA -- Accessing Domain-Specific FAQs via Conversational QA
We present DoQA, a dataset with 2, 437 dialogues and 10, 917 QA pairs.
DoQA - Accessing Domain-Specific FAQs via Conversational QA
We present DoQA, a dataset with 2, 437 dialogues and 10, 917 QA pairs.
Spot The Bot: A Robust and Efficient Framework for the Evaluation of Conversational Dialogue Systems
In this work, we introduce \emph{Spot The Bot}, a cost-efficient and robust evaluation framework that replaces human-bot conversations with conversations between bots.
Probing the Robustness of Trained Metrics for Conversational Dialogue Systems
This paper introduces an adversarial method to stress-test trained metrics to evaluate conversational dialogue systems.
Report from the NSF Future Directions Workshop on Automatic Evaluation of Dialog: Research Directions and Challenges
This is a report on the NSF Future Directions Workshop on Automatic Evaluation of Dialog.
SDS-200: A Swiss German Speech to Standard German Text Corpus
We present SDS-200, a corpus of Swiss German dialectal speech with Standard German text translations, annotated with dialect, age, and gender information of the speakers.
On the Effectiveness of Automated Metrics for Text Generation Systems
A major challenge in the field of Text Generation is evaluation because we lack a sound theory that can be leveraged to extract guidelines for evaluation campaigns.
STT4SG-350: A Speech Corpus for All Swiss German Dialect Regions
We train an ASR model on the training set and achieve an average BLEU score of 74. 7 on the test set.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Text-to-Speech Pipeline for Swiss German -- A comparison
In this work, we studied the synthesis of Swiss German speech using different Text-to-Speech (TTS) models.
Correction of Errors in Preference Ratings from Automated Metrics for Text Generation
A major challenge in the field of Text Generation is evaluation: Human evaluations are cost-intensive, and automated metrics often display considerable disagreement with human judgments.
ScienceBenchmark: A Complex Real-World Benchmark for Evaluating Natural Language to SQL Systems
Thus, the challenge is many-fold: creating NL-to-SQL systems for highly complex domains with a small amount of hand-made training data augmented with synthetic data.
Dialect Transfer for Swiss German Speech Translation
This paper investigates the challenges in building Swiss German speech translation systems, specifically focusing on the impact of dialect diversity and differences between Swiss German and Standard German.
Are We Summarizing the Right Way? A Survey of Dialogue Summarization Data Sets
Dialogue summarization is a long-standing task in the field of NLP, and several data sets with dialogues and associated human-written summaries of different styles exist.
