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Found 11 papers, 5 papers with code
PairEval: Open-domain Dialogue Evaluation with Pairwise Comparison
Recent studies proposed evaluation metrics that assess generated responses by considering their relevance to previous dialogue histories.
Learning to Diversify Neural Text Generation via Degenerative Model
We then enhance the diversity of the second model by focusing on patterns that the first model fails to learn.
DEnsity: Open-domain Dialogue Evaluation Metric using Density Estimation
Despite the recent advances in open-domain dialogue systems, building a reliable evaluation metric is still a challenging problem.
Pneg: Prompt-based Negative Response Generation for Dialogue Response Selection Task
To overcome these limitations, this paper proposes a simple but efficient method for generating adversarial negative responses leveraging a large-scale language model.
Reweighting Strategy based on Synthetic Data Identification for Sentence Similarity
To analyze this, we first train a classifier that identifies machine-written sentences, and observe that the linguistic features of the sentences identified as written by a machine are significantly different from those of human-written sentences.
Evaluating Predictive Uncertainty under Distributional Shift on Dialogue Dataset
In open-domain dialogues, predictive uncertainties are mainly evaluated in a domain shift setting to cope with out-of-distribution inputs.
Generating Negative Samples by Manipulating Golden Responses for Unsupervised Learning of a Response Evaluation Model
Reference-based metrics that rely on comparisons to a set of known correct responses often fail to account for this variety, and consequently correlate poorly with human judgment.
Unsupervised Document Expansion for Information Retrieval with Stochastic Text Generation
In this paper, we propose an Unsupervised Document Expansion with Generation (UDEG) framework with a pre-trained language model, which generates diverse supplementary sentences for the original document without using labels on query-document pairs for training.
An Empirical Study on Measuring the Similarity of Sentential Arguments with Language Model Domain Adaptation
Measuring the similarity between two different sentential arguments is an important task in argument mining.
Generating Sentential Arguments from Diverse Perspectives on Controversial Topic
Considering diverse aspects of an argumentative issue is an essential step for mitigating a biased opinion and making reasonable decisions.
