| Challenge: | Existing neural end-to-end dialogue models have limitations on exactly interpreting the linguistic structures in dialogue history context. |
| Approach: | They propose to directly measure the capability of neural end-to-end dialogue models on understanding the entity-oriented structures via question answering. |
| Outcome: | The proposed model can understand large-scale English and Chinese human human dialogues using a large-format dataset. |
Similar Papers
CoQAR: Question Rewriting on CoQA (2022.lrec-1)
Copied to clipboard
| Challenge: | Existing systems that ask questions in a conversational context may have contextual dependencies that make the understanding difficult. |
| Approach: | They propose to rewrite questions into an out-of-context form to facilitate understanding . they propose to use this form to train and evaluate conversational question answering models . |
| Outcome: | The proposed model can be used in the supervised learning of three tasks: question paraphrasing, question rewriting and conversational question answering. |
End-to-End Neural Discourse Deixis Resolution in Dialogue (2022.emnlp-main)
Copied to clipboard
| Challenge: | Lexical overlap is a strong indicator of entity coreference, both among names and in the resolution of nominals. |
| Approach: | They propose to extend their span-based entity coreference model to exploit task-specific characteristics of discourse deixis resolution in dialogue. |
| Outcome: | The proposed model achieves state-of-the-art results on the four datasets in the CODI-CRAC 2021 shared task. |
CREAD: Combined Resolution of Ellipses and Anaphora in Dialogues (2021.naacl-main)
Copied to clipboard
Bo-Hsiang Tseng, Shruti Bhargava, Jiarui Lu, Joel Ruben Antony Moniz, Dhivya Piraviperumal, Lin Li, Hong Yu
| Challenge: | Traditionally, anaphora resolution and ellipses resolution are limited in dialogues . despite rapid progress in dialogue systems, several difficulties remain . |
| Approach: | They propose a joint learning framework for modeling coreference resolution and query rewriting for complex, multi-turn dialogues. |
| Outcome: | The proposed model outperforms the state-of-the-art model on a rewritten dialogue dataset. |
How coherent are neural models of coherence? (2020.coling-main)
Copied to clipboard
| Challenge: | Existing approaches to model coherence are limited to small newswire corpora . evaluators need to be trained on lexical and document levels to perform evaluations . |
| Approach: | They propose four generic evaluation tasks that capture coherence-specific properties . they aim at capturing correct use of discourse connectives and lexical cohesion . |
| Outcome: | The proposed tasks capture coherence-specific properties, including correct use of discourse connectives, lexical cohesion, temporal consistency among events and participants in a story. |
Is Incoherence Surprising? Targeted Evaluation of Coherence Prediction from Language Models (2021.naacl-main)
Copied to clipboard
| Challenge: | a common approach to coherence evaluation is shuffling the sentence order of a text, creating incoherent text samples that need to be discriminated from the original. |
| Approach: | They propose an extendable set of test suites addressing different aspects of discourse and dialogue coherence. |
| Outcome: | The proposed evaluation paradigm is suited to evaluate linguistic qualities that contribute to the notion of coherence. |
GECOR: An End-to-End Generative Ellipsis and Co-reference Resolution Model for Task-Oriented Dialogue (D19-1)
Copied to clipboard
| Challenge: | Ellipsis and co-reference are common and ubiquitous especially in multi-turn dialogues. |
| Approach: | They propose a unified end-to-end Generative Ellipsis and CO-reference Resolution model . the model can generate a new pragmatically complete user utterance by alternating the generation and copy mode for each user . |
| Outcome: | The proposed model can generate a new pragmatically complete user utterance by alternating the generation and copy mode for each user . intrinsic evaluations on the resolution of ellipsis and co-reference show that the model outperforms the baseline model in terms of EM, BLEU and F1 . |
DialSummEval: Revisiting Summarization Evaluation for Dialogues (2022.naacl-main)
Copied to clipboard
| Challenge: | Current models for dialogue summarization have flaws that may not be well exposed by frequently used metrics such as ROUGE. |
| Approach: | They propose to re-evaluate 18 categories of metrics in terms of four dimensions: coherence, consistency, fluency and relevance, as well as a unified human evaluation of various models for the first time. |
| Outcome: | The proposed dataset will be used to evaluate 18 categories of metrics in terms of coherence, consistency, fluency and relevance, and a unified human evaluation of various models for the first time. |
Structural Characterization for Dialogue Disentanglement (2022.acl-long)
Copied to clipboard
| Challenge: | tangled multi-party dialogues lead to difficulties in understanding the dialogue history for both human and machine. |
| Approach: | They propose a model for disentangling multi-party dialogues using speaker property and reference dependency. |
| Outcome: | The proposed model achieves state-of-the-art on the Ubuntu IRC benchmark dataset and contributes to dialogue-related comprehension. |
Dialogue Coherence Assessment Without Explicit Dialogue Act Labels (2020.acl-main)
Copied to clipboard
| Challenge: | Recent dialogue coherence models use coherency features designed for monologue texts to represent utterances and then explicitly augment them with dialogue-relevant features, e.g., dialogue act labels. |
| Approach: | They propose a multi-task learning approach that uses dialogue act prediction to obtain informative utterance representations for coherence assessment. |
| Outcome: | The proposed model outperforms its strong competitors on the DailyDialogue corpus and performs on par with them on the SwitchBoard corpus for ranking dialogues concerning their coherence. |
Treating Dialogue Quality Evaluation as an Anomaly Detection Problem (2020.lrec-1)
Copied to clipboard
| Challenge: | Dialogue systems for interaction with humans are becoming more popular . the best way to estimate their success is through means of human evaluation . |
| Approach: | They investigate the effectiveness of perceiving dialogue evaluation as an anomaly detection task. |
| Outcome: | The proposed approach is based on four models and shows negative results . the proposed approach could be used in the future to improve human-led dialogue evaluations. |