kogito: A Commonsense Knowledge Inference Toolkit
Not on the map. Why?
Abstract (arXiv)
In this paper, we present kogito, an open-source tool for generating commonsense inferences about situations described in text. kogito provides an intuitive and extensible interface to interact with natural language generation models that can be used for hypothesizing commonsense knowledge inference from a textual input. In particular, kogito offers several features for targeted, multi-granularity knowledge generation. These include a standardized API for training and evaluating knowledge models, and generating and filtering inferences from them. We also include helper functions for converting natural language texts into a format ingestible by knowledge models - intermediate pipeline stages such as knowledge head extraction from text, heuristic and model-based knowledge head-relation matching, and an ability to define and use custom knowledge relations. We make the code for kogito available at https://github.com/epfl-nlp/kogito along with thorough documentation at https://kogito.readthedocs.io.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 23 of its 37 references that are in the map. First posted on arXiv 15 Nov 2022.
Builds on
23 of 37 references.
References · 23
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2020Language Models are Few-Shot Learnerscited by 32,419
- 2019BERTScore: Evaluating Text Generation with BERTcited by 5,512
- 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,307
- 2014CIDEr: Consensus-based Image Description Evaluationcited by 2,046
- 2019Language Models as Knowledge Bases?cited by 1,936
- 2020RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Modelscited by 1,255
- 2021Ethical and social risks of harm from Language Modelscited by 966
- 2019Universal Adversarial Triggers for Attacking and Analyzing NLPcited by 722
- 2020How Much Knowledge Can You Pack Into the Parameters of a Language Model?cited by 719
- 2019COMET: Commonsense Transformers for Automatic Knowledge Graph Constructioncited by 450
11 more
- 2021Symbolic Knowledge Distillation: from General Language Models to Commonsense Modelscited by 270
- 2020COMET-ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphscited by 254
- 2020Towards Controllable Biases in Language Generationcited by 112
- 2018Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanismscited by 53
- 2020Like hiking? You probably enjoy nature: Persona-grounded Dialog with Commonsense Expansionscited by 49
- 2020Automated Storytelling via Causal, Commonsense Plot Orderingcited by 38
- 2021Analyzing Commonsense Emergence in Few-shot Knowledge Modelscited by 34
- 2020R3: Reverse, Retrieve, and Rank for Sarcasm Generation with Commonsense Knowledgecited by 32
- 2021"I'm Not Mad": Commonsense Implications of Negation and Contradictioncited by 26
- 2020Playing Text-Based Games with Common Sensecited by 21
- 2022ComFact: A Benchmark for Linking Contextual Commonsense Knowledgecited by 11
Built on by
Citing papers per year: 2023: 2; 2024: 2.
4 papers.
- 2023PeaCoK: Persona Commonsense Knowledge for Consistent and Engaging Narrativescited by 19
- 2024Neural Multimodal Topic Modeling: A Comprehensive Evaluationcited by 5
- 2024Can Language Models Take A Hint? Prompting for Controllable Contextualized Commonsense Inferencecited by 1
- 2023Profiling the news spreading barriers using news headlinescited by 0
- 2024Can Language Models Take A Hint? Prompting for Controllable Contextualized Commonsense Inference3 Oct 2024
- 2024Neural Multimodal Topic Modeling: A Comprehensive Evaluation26 Mar 2024
- 2023PeaCoK: Persona Commonsense Knowledge for Consistent and Engaging Narratives3 May 2023
- 2023Profiling the news spreading barriers using news headlines7 Apr 2023