Topic-to-Essay Generation with Neural Networks

By Xiaocheng Feng, Ming Liu, Jiahao Liu, Bing Qin, Yibo Sun, Ting-Wei Liu. 2018

72 citations recorded by the source corpus.

We focus on essay generation, which is a challenging task that generates a paragraph-level text with multiple topics.Progress towards understanding different topics and expressing diversity in this task requires more powerful generators and richer training and evaluation resources. To address this,  we develop a multi-topic aware long short-term memory (MTA-LSTM) network.In this model, we maintain a novel multi-topic coverage vector, which learns the weight of each topic and is sequentially updated during the decoding process.Afterwards this vector is fed to an attention model to guide the generator.Moreover, we automatically construct two paragraph-level Chinese essay corpora, 305,000 essay paragraphs and 55,000 question-and-answer pairs.Empirical results show that our approach obtains much better BLEU score compared to various baselines.Furthermore, human judgment shows that MTA-LSTM has the ability to generate essays that are not only coherent but also closely related to the input topics.

Subfields: Artificial Intelligence, Natural Language Processing, Generative Models

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xiang Ren
2. Yejin Choi
3. Bill Yuchen Lin
4. Chandra Bhagavatula
5. Pei Zhou
6. Hannaneh Hajishirzi
7. Mohit Bansal
8. Wangchunshu Zhou

In Natural Language Processing

1. Yejin Choi
2. Xiang Ren
3. Chandra Bhagavatula
4. Bill Yuchen Lin
5. Pei Zhou
6. Wangchunshu Zhou
7. Hannaneh Hajishirzi
8. Ming Shen

In Generative Models

1. Xiang Ren
2. Wangchunshu Zhou
3. Yejin Choi
4. Chandra Bhagavatula

Citations in the corpus

• LongForm: Effective Instruction Tuning with Reverse Instructions (2023, neutral): “Many works do task-specific generation (e.g., generation of stories (Fan et al., 2018), recipes (Bie´n et al., 2020), essays (Feng et al., 2018) and long-form QA (Fan et al., 2019)) by collecting useful examples from existing resources (e.g., ELI5 sub-reddit, WritingPrompts subreddit, recipe…”
• CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning (2020, background): “…text generation aims to decode sentences with expected attributes such as sentiment (Luo et al., 2019a; Hu et al., 2017), tense (Hu et al., 2017), template (Zhu et al., 2019; J Kurisinkel and Chen, 2019), style (Fu et al., 2018; Luo et al., 2019b; Li et al., 2018), topics (Feng et al., 2018), etc.”