StalemateBreaker: A Proactive Content-Introducing Approach to Automatic Human-Computer Conversation
Authors: Xiang Li, Lili Mou, Rui Yan, Ming Zhang
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our system on conversation logs from real-world users. Our approach outperforms several strong baselines as well as a state-of-the-practice system. ... We hired workers on a Chinese crowdsourcing platform to annotate all retrieved results with 1 Point (appropriate) or 0 Point (inappropriate). ... Table 1 shows the performance of our STALEMATEBREAKER system as well as a variety of baselines. |
| Researcher Affiliation | Collaboration | Xiang Li,1, Lili Mou,1,2 Rui Yan,3 Ming Zhang1 1School of EECS, Peking University, China {lixiang.eecs,mzhang cs}@pku.edu.cn 2Key Laboratory of High Confidence Software Technologies (Peking University), Ministry of Education, China doublepower.mou@gmail.com 3Natural Language Processing Department, Baidu Inc., China yanrui02@baidu.com |
| Pseudocode | Yes | Algorithm 1: Bi-Page Rank-HITS |
| Open Source Code | No | The paper does not include an explicit statement about releasing source code for the described methodology or a direct link to a code repository. |
| Open Datasets | No | The paper mentions collecting data from various platforms (Baidu Zhidao, Baidu Tieba, Douban forum, Sina Weibo) and using a knowledge graph mined from Baidu search logs, but it does not provide a direct URL, DOI, or specific repository name for accessing the collected dataset itself. |
| Dataset Splits | No | The paper describes the data collection and annotation process for evaluation but does not specify exact training, validation, or test dataset split percentages, sample counts, or refer to predefined splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU or CPU models, memory specifications) used to run the experiments. |
| Software Dependencies | No | The paper mentions using standard tools and models like Lucene, word2vec, and a learning-to-rank model, but it does not specify version numbers for any software dependencies. |
| Experiment Setup | Yes | In our Bi-Page Rank-HITS model, we have three main parameters, µ in the Page Rank phase, and x, y in the HITS phase. µ was set to 0.15 following Yan et al. [2012a] and not tuned in our experiment. For x and y, we tried different values with a granularity of 0.1. |