Review of When Less Is More: Using Less Context Information to Generate Better Utterances in Group Conversations
By: Mouaad El Meslouhi
Tags: Machine Learning, Gru, Natural Language Process, Deep Learning, Conversations

Review of When Less Is More: Using Less Context Information to Generate Better Utterances in Group Conversations Mouaad El Meslouhi Oct 5, 2019·3 min read

In the last decade researchers worked vigorously to find a way to simulate human to human conversation. The dialogue system between two participants is rather simple. However, in conversations within companies, most dialogues include multiple participants “Group Conversations”. In this case we cannot cover all scenarios.

In the paper [1] at hand researchers proposed a tree-based model frame for structure-aware conversation to produce responses (generation-based approach [2,3,4,5]).

The conversation is rebuilt in a tree structure and afterwards divided into sequences, these sequences are basically encoded information, destined to generate the target utterance following the implementation of the model with gated recurrent units (GRU) [6] based on the hierarchical representation [6,7].

The public ubuntu corpus [8] was used for training, testing and validation of the model. To evaluate the model, multiple baselines were considered for comparison: neural responding machine (NRM) [9], Context-Seq2Seq [10] and The Hierarchical Recurrent Encoder-Decoder (HRED) model [7]

None of those baselines take the structure of the group conversation into consideration. To evaluate the performance of the model against baselines the package released by [8], was used. As a result, the model outperformed baselines in all metrics. This goes to show that the structure of the conversation is utterly important.

To sum up, an adequately structured conversation with insightful content results in a better performing model.

References :

1. Zhang, Haisong, Zhangming Chan, Yan Song, Dongyan Zhao, and Rui Yan. 2018. “When Less Is More: Using Less Context Information To Generate Better Utterances In Group Conversations”. Natural Language Processing And Chinese Computing, 76–84. doi:10.1007/978–3–319–99495–6_7.

2. Li, J., Galley, M., Brockett, C., Spithourakis, G., Gao, J., Dolan, B.: A personabased neural conversation model. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), vol. 1, pp. 994–1003 (2016).

3. Shang, L., Lu, Z., Li, H.: Neural responding machine for short-text conversation. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), vol. 1, pp. 1577–1586 (2015).

4. Song, Y., Yan, R., Feng, Y., Zhang, Y., Zhao, D., Zhang, M.: Towards a neural conversation model with diversity net using determinantal point processes. In: AAAI 2018, pp. 5932–5939 (2018).

5. Vinyals, O., Le, Q.: A neural conversational model. arXiv preprint arXiv:1506.05869 (2015).

6. Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: ICLR (2015).

7. Serban, I.V., Sordoni, A., Bengio, Y., Courville, A.C., Pineau, J.: Building endto-end dialogue systems using generative hierarchical neural network models. In: AAAI, pp. 3776–3784 (2016).

8. Lowe, R.J., Pow, N., Serban, I., Pineau, J.: The ubuntu dialogue corpus: a large dataset for research in unstructured multi-turn dialogue systems. In: SIGDIAL Conference (2015).

9. Shang, L., Lu, Z., Li, H.: Neural responding machine for short-text conversation. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), vol. 1, pp. 1577–1586 (2015).

10. Sordoni, A., Galley, M., Auli, M., Brockett, C., Ji, Y., Mitchell, M., Nie, J.Y., Gao, J., Dolan, B.: A neural network approach to context-sensitive generation of conversational responses. In: Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 196–205 (2015).

11. Chen, X., Fang, H., Lin, T.Y., Vedantam, R., Gupta, S., Dollar, P., Zitnick, C.L.: Microsoft coco captions: data collection and evaluation server. CoRR abs/1504.00325 (2015).