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Found 23 papers, 9 papers with code
Investigating Content Planning for Navigating Trade-offs in Knowledge-Grounded Dialogue
Knowledge-grounded dialogue generation is a challenging task because it requires satisfying two fundamental yet often competing constraints: being responsive in a manner that is specific to what the conversation partner has said while also being attributable to an underlying source document.
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models.
Measuring Attribution in Natural Language Generation Models
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.
CONQRR: Conversational Query Rewriting for Retrieval with Reinforcement Learning
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.
Increasing Faithfulness in Knowledge-Grounded Dialogue with Controllable Features
At training time, additional inputs based on these evaluation measures are given to the dialogue model.
Evaluating Attribution in Dialogue Systems: The BEGIN Benchmark
To this end, we introduce the Benchmark for Evaluation of Grounded INteraction (BEGIN), comprised of 12k dialogue turns generated by neural dialogue systems trained on three knowledge-grounded dialogue corpora.
PowerTransformer: Unsupervised Controllable Revision for Biased Language Correction
Unconscious biases continue to be prevalent in modern text and media, calling for algorithms that can assist writers with bias correction.
PlotMachines: Outline-Conditioned Generation with Dynamic Plot State Tracking
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.
Social IQa: Commonsense Reasoning about Social Interactions
We introduce Social IQa, the first large-scale benchmark for commonsense reasoning about social situations.
COMET: Commonsense Transformers for Automatic Knowledge Graph Construction
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).
Defending Against Neural Fake News
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.
Ranked #2 on
Fake News Detection
on Grover-Mega
I Know the Feeling: Learning to Converse with Empathy
Beyond understanding what is being discussed, human communication requires an awareness of what someone is feeling.
SocialIQA: Commonsense Reasoning about Social Interactions
We introduce Social IQa, the first largescale benchmark for commonsense reasoning about social situations.
Ranked #9 on
Question Answering
on SIQA
Towards Empathetic Open-domain Conversation Models: a New Benchmark and Dataset
One challenge for dialogue agents is recognizing feelings in the conversation partner and replying accordingly, a key communicative skill.
ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning
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
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.
Ranked #1 on
Common Sense Reasoning
on Event2Mind test
Modeling Naive Psychology of Characters in Simple Commonsense Stories
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.
Ranked #2 on
Emotion Classification
on ROCStories
Truth of Varying Shades: Analyzing Language in Fake News and Political Fact-Checking
We present an analytic study on the language of news media in the context of political fact-checking and fake news detection.
Connotation Frames of Power and Agency in Modern Films
The framing of an action influences how we perceive its actor.
Multilingual Connotation Frames: A Case Study on Social Media for Targeted Sentiment Analysis and Forecast
People around the globe respond to major real world events through social media.
Connotation Frames: A Data-Driven Investigation
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.
