Search Results for author: Hannah Rashkin

Found 21 papers, 6 papers with code

Measuring Attribution in Natural Language Generation Models

no code implementations23 Dec 2021 Hannah Rashkin, Vitaly Nikolaev, Matthew Lamm, Michael Collins, Dipanjan Das, Slav Petrov, Gaurav Singh Tomar, Iulia Turc, David Reitter

With recent improvements in natural language generation (NLG) models for various applications, it has become imperative to have the means to identify and evaluate whether NLG output is only sharing verifiable information about the external world.

Text Generation

CONQRR: Conversational Query Rewriting for Retrieval with Reinforcement Learning

no code implementations16 Dec 2021 Zeqiu Wu, Yi Luan, Hannah Rashkin, David Reitter, Hannaneh Hajishirzi, Mari Ostendorf, Gaurav Singh Tomar

Compared to standard retrieval tasks, passage retrieval for conversational question answering (CQA) poses new challenges in understanding the current user question, as each question needs to be interpreted within the dialogue context.

Conversational Question Answering Passage Retrieval +1

Evaluating Groundedness in Dialogue Systems: The BEGIN Benchmark

no code implementations30 Apr 2021 Nouha Dziri, Hannah Rashkin, Tal Linzen, David Reitter

To facilitate evaluation of such metrics, we introduce the Benchmark for Evaluation of Grounded INteraction (BEGIN).

Language Modelling Natural Language Inference

PowerTransformer: Unsupervised Controllable Revision for Biased Language Correction

no code implementations EMNLP 2020 Xinyao Ma, Maarten Sap, Hannah Rashkin, Yejin Choi

Unconscious biases continue to be prevalent in modern text and media, calling for algorithms that can assist writers with bias correction.

Pretrained Language Models

PlotMachines: Outline-Conditioned Generation with Dynamic Plot State Tracking

2 code implementations EMNLP 2020 Hannah Rashkin, Asli Celikyilmaz, Yejin Choi, Jianfeng Gao

We propose the task of outline-conditioned story generation: given an outline as a set of phrases that describe key characters and events to appear in a story, the task is to generate a coherent narrative that is consistent with the provided outline.

Story Generation

COMET: Commonsense Transformers for Automatic Knowledge Graph Construction

2 code implementations ACL 2019 Antoine Bosselut, Hannah Rashkin, Maarten Sap, Chaitanya Malaviya, Asli Celikyilmaz, Yejin Choi

We present the first comprehensive study on automatic knowledge base construction for two prevalent commonsense knowledge graphs: ATOMIC (Sap et al., 2019) and ConceptNet (Speer et al., 2017).

graph construction Knowledge Graphs

Defending Against Neural Fake News

4 code implementations NeurIPS 2019 Rowan Zellers, Ari Holtzman, Hannah Rashkin, Yonatan Bisk, Ali Farhadi, Franziska Roesner, Yejin Choi

We find that best current discriminators can classify neural fake news from real, human-written, news with 73% accuracy, assuming access to a moderate level of training data.

Fake News Detection Text Generation

I Know the Feeling: Learning to Converse with Empathy

no code implementations ICLR 2019 Hannah Rashkin, Eric Michael Smith, Margaret Li, Y-Lan Boureau

Beyond understanding what is being discussed, human communication requires an awareness of what someone is feeling.

Dialogue Generation

Towards Empathetic Open-domain Conversation Models: a New Benchmark and Dataset

8 code implementations ACL 2019 Hannah Rashkin, Eric Michael Smith, Margaret Li, Y-Lan Boureau

One challenge for dialogue agents is recognizing feelings in the conversation partner and replying accordingly, a key communicative skill.

Dialogue Generation

ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning

2 code implementations31 Oct 2018 Maarten Sap, Ronan LeBras, Emily Allaway, Chandra Bhagavatula, Nicholas Lourie, Hannah Rashkin, Brendan Roof, Noah A. Smith, Yejin Choi

We present ATOMIC, an atlas of everyday commonsense reasoning, organized through 877k textual descriptions of inferential knowledge.

Event2Mind: Commonsense Inference on Events, Intents, and Reactions

no code implementations ACL 2018 Hannah Rashkin, Maarten Sap, Emily Allaway, Noah A. Smith, Yejin Choi

We investigate a new commonsense inference task: given an event described in a short free-form text ("X drinks coffee in the morning"), a system reasons about the likely intents ("X wants to stay awake") and reactions ("X feels alert") of the event's participants.

Common Sense Reasoning

Modeling Naive Psychology of Characters in Simple Commonsense Stories

no code implementations ACL 2018 Hannah Rashkin, Antoine Bosselut, Maarten Sap, Kevin Knight, Yejin Choi

Understanding a narrative requires reading between the lines and reasoning about the unspoken but obvious implications about events and people's mental states - a capability that is trivial for humans but remarkably hard for machines.

Emotion Classification

Connotation Frames: A Data-Driven Investigation

no code implementations ACL 2016 Hannah Rashkin, Sameer Singh, Yejin Choi

Through a particular choice of a predicate (e. g., "x violated y"), a writer can subtly connote a range of implied sentiments and presupposed facts about the entities x and y: (1) writer's perspective: projecting x as an "antagonist"and y as a "victim", (2) entities' perspective: y probably dislikes x, (3) effect: something bad happened to y, (4) value: y is something valuable, and (5) mental state: y is distressed by the event.

Cannot find the paper you are looking for? You can Submit a new open access paper.