Search Results for author:
Found 13 papers, 3 papers with code
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.
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.
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.
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.
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.
LEDGAR: A Large-Scale Multi-label Corpus for Text Classification of Legal Provisions in Contracts
We present LEDGAR, a multilabel corpus of legal provisions in contracts.
A method for in-depth comparative evaluation: How (dis)similar are outputs of pos taggers, dependency parsers and coreference resolvers really?
This paper proposes a generic method for the comparative evaluation of system outputs.
Machine Translation of Spanish Personal and Possessive Pronouns Using Anaphora Probabilities
We implement a fully probabilistic model to combine the hypotheses of a Spanish anaphora resolution system with those of a Spanish-English machine translation system.
Co-reference Resolution of Elided Subjects and Possessive Pronouns in Spanish-English Statistical Machine Translation
This paper presents a straightforward method to integrate co-reference information into phrase-based machine translation to address the problems of i) elided subjects and ii) morphological underspecification of pronouns when translating from pro-drop languages.
Stance Detection in Facebook Posts of a German Right-wing Party
We argue that in order to detect stance, not only the explicit attitudes of the stance holder towards the targets are crucial.
