# Recurrent Convolutional Neural Networks for Discourse Compositionality

arXiv: https://arxiv.org/abs/1306.3584 · HTML: https://mlsanity.com/paper/1306.3584/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2013.

## Built on by (most cited first, 7 of 7)

- [Dialog Acts for Task-Driven Embodied Agents](https://mlsanity.com/paper/2209.12953/) (2022)
- [A Neural Question Answering System for Basic Questions about Subroutines](https://mlsanity.com/paper/2101.03999/) (2021)
- [Implicit N-grams Induced by Recurrence](https://mlsanity.com/paper/2205.02724/) (2022)
- [Can we learn from developer mistakes? Learning to localize and repair real bugs from real bug fixes](https://mlsanity.com/paper/2207.00301/) (2022)
- [A Universality-Individuality Integration Model for Dialog Act Classification](https://mlsanity.com/paper/2204.06185/) (2022)
- [Sentence Ambiguity, Grammaticality and Complexity Probes](https://mlsanity.com/paper/2210.06928/) (2022)
- [Optimal Kernel Tuning Parameter Prediction using Deep Sequence Models](https://mlsanity.com/paper/2404.10162/) (2024)

## Abstract (arXiv)

The compositionality of meaning extends beyond the single sentence. Just as words combine to form the meaning of sentences, so do sentences combine to form the meaning of paragraphs, dialogues and general discourse. We introduce both a sentence model and a discourse model corresponding to the two levels of compositionality. The sentence model adopts convolution as the central operation for composing semantic vectors and is based on a novel hierarchical convolutional neural network. The discourse model extends the sentence model and is based on a recurrent neural network that is conditioned in a novel way both on the current sentence and on the current speaker. The discourse model is able to capture both the sequentiality of sentences and the interaction between different speakers. Without feature engineering or pretraining and with simple greedy decoding, the discourse model coupled to the sentence model obtains state of the art performance on a dialogue act classification experiment.