Search Results for author: Christopher Manning

Found 32 papers, 4 papers with code

Effective Social Chatbot Strategies for Increasing User Initiative

no code implementations SIGDIAL (ACL) 2021 Amelia Hardy, Ashwin Paranjape, Christopher Manning

Many existing chatbots do not effectively support mixed initiative, forcing their users to either respond passively or lead constantly.


Understanding and predicting user dissatisfaction in a neural generative chatbot

no code implementations SIGDIAL (ACL) 2021 Abigail See, Christopher Manning

We find that unclear user utterances are a major source of generative errors such as ignoring, hallucination, unclearness and repetition.

Chatbot Hallucination

Challenges for Information Extraction from Dialogue in Criminal Law

no code implementations ACL (NLP4PosImpact) 2021 Jenny Hong, Catalin Voss, Christopher Manning

Information extraction and question answering have the potential to introduce a new paradigm for how machine learning is applied to criminal law.

Question Answering

Machine Translation for Nko: Tools, Corpora and Baseline Results

2 code implementations24 Oct 2023 Moussa Koulako Bala Doumbouya, Baba Mamadi Diané, Solo Farabado Cissé, Djibrila Diané, Abdoulaye Sow, Séré Moussa Doumbouya, Daouda Bangoura, Fodé Moriba Bayo, Ibrahima Sory 2. Condé, Kalo Mory Diané, Chris Piech, Christopher Manning

Currently, there is no usable machine translation system for Nko, a language spoken by tens of millions of people across multiple West African countries, which holds significant cultural and educational value.

Machine Translation Translation

JamPatoisNLI: A Jamaican Patois Natural Language Inference Dataset

no code implementations7 Dec 2022 Ruth-Ann Armstrong, John Hewitt, Christopher Manning

While our work, along with previous work, shows that transfer from these models to low-resource languages that are unrelated to languages in their training set is not very effective, we would expect stronger results from transfer to creoles.

Cross-Lingual Transfer Few-Shot Learning +1

DReCa: A General Task Augmentation Strategy for Few-Shot Natural Language Inference

no code implementations NAACL 2021 Shikhar Murty, Tatsunori B. Hashimoto, Christopher Manning

Meta-learning promises few-shot learners that can adapt to new distributions by repurposing knowledge acquired from previous training.

Clustering Few-Shot NLI +2

Towards Ecologically Valid Research on Language User Interfaces

no code implementations28 Jul 2020 Harm de Vries, Dzmitry Bahdanau, Christopher Manning

To this end, we describe what we deem an ideal methodology for machine learning research on LUIs and categorize five common ways in which recent benchmarks deviate from it.

BIG-bench Machine Learning valid

Robust Subgraph Generation Improves Abstract Meaning Representation Parsing

no code implementations IJCNLP 2015 Keenon Werling, Gabor Angeli, Christopher Manning

The Abstract Meaning Representation (AMR) is a representation for open-domain rich semantics, with potential use in fields like event extraction and machine translation.

AMR Parsing Event Extraction +2

Event Extraction Using Distant Supervision

no code implementations LREC 2014 Kevin Reschke, Martin Jankowiak, Mihai Surdeanu, Christopher Manning, Daniel Jurafsky

We present a new publicly available dataset and event extraction task in the plane crash domain based on Wikipedia infoboxes and newswire text.

Event Extraction Knowledge Base Population +2

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