# Automated Speaking Assessment of Conversation Tests with Novel Graph-based Modeling on Spoken Response Coherence

arXiv: https://arxiv.org/abs/2409.07064 · HTML: https://mlsanity.com/paper/2409.07064/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 43 references that are in the map.
- First posted on arXiv 11 Sep 2024.

## Builds on (most cited first, 13 of 13)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [Heterogeneous Graph Neural Networks for Extractive Document Summarization](https://mlsanity.com/paper/2004.12393/) (2020)
- [Dialogue Discourse-Aware Graph Model and Data Augmentation for Meeting Summarization](https://mlsanity.com/paper/2012.03502/) (2020)
- [Coherence Models for Dialogue](https://mlsanity.com/paper/1806.08044/) (2018)
- [Modeling Fine-grained Information via Knowledge-aware Hierarchical Graph for Zero-shot Entity Retrieval](https://mlsanity.com/paper/2211.10991/) (2022)
- [Cross-utterance Reranking Models with BERT and Graph Convolutional Networks for Conversational Speech Recognition](https://mlsanity.com/paper/2106.06922/) (2021)
- [Structured Dialogue Discourse Parsing](https://mlsanity.com/paper/2306.15103/) (2023)
- [Speaker Fluency Level Classification Using Machine Learning Techniques](https://mlsanity.com/paper/1808.10556/) (2018)

## Built on by (most cited first, 1 of 1)

- [Automatic Proficiency Assessment in L2 English Learners](https://mlsanity.com/paper/2505.02615/) (2025)

## Abstract (arXiv)

Automated speaking assessment in conversation tests (ASAC) aims to evaluate the overall speaking proficiency of an L2 (second-language) speaker in a setting where an interlocutor interacts with one or more candidates. Although prior ASAC approaches have shown promising performance on their respective datasets, there is still a dearth of research specifically focused on incorporating the coherence of the logical flow within a conversation into the grading model. To address this critical challenge, we propose a hierarchical graph model that aptly incorporates both broad inter-response interactions (e.g., discourse relations) and nuanced semantic information (e.g., semantic words and speaker intents), which is subsequently fused with contextual information for the final prediction. Extensive experimental results on the NICT-JLE benchmark dataset suggest that our proposed modeling approach can yield considerable improvements in prediction accuracy with respect to various assessment metrics, as compared to some strong baselines. This also sheds light on the importance of investigating coherence-related facets of spoken responses in ASAC.