Search Results for author:
Found 41 papers, 25 papers with code
A latent factor model for highly multi-relational data
While there is a large body of work focused on modeling these data, few considered modeling these multiple types of relationships jointly.
A Semantic Matching Energy Function for Learning with Multi-relational Data
Large-scale relational learning becomes crucial for handling the huge amounts of structured data generated daily in many application domains ranging from computational biology or information retrieval, to natural language processing.
Irreflexive and Hierarchical Relations as Translations
We consider the problem of embedding entities and relations of knowledge bases in low-dimensional vector spaces.
Connecting Language and Knowledge Bases with Embedding Models for Relation Extraction
This paper proposes a novel approach for relation extraction from free text which is trained to jointly use information from the text and from existing knowledge.
Translating Embeddings for Modeling Multi-relational Data
We consider the problem of embedding entities and relationships of multi-relational data in low-dimensional vector spaces.
Ranked #5 on
Link Prediction
on FB122
Open Question Answering with Weakly Supervised Embedding Models
Building computers able to answer questions on any subject is a long standing goal of artificial intelligence.
Ranked #1 on
Question Answering
on Reverb
Question Answering with Subgraph Embeddings
Training our system using pairs of questions and structured representations of their answers, and pairs of question paraphrases, yields competitive results on a competitive benchmark of the literature.
Ranked #2 on
Question Answering
on WebQuestions
(F1 metric)
Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks
One long-term goal of machine learning research is to produce methods that are applicable to reasoning and natural language, in particular building an intelligent dialogue agent.
Combining Two And Three-Way Embeddings Models for Link Prediction in Knowledge Bases
This paper tackles the problem of endogenous link prediction for Knowledge Base completion.
Large-scale Simple Question Answering with Memory Networks
Training large-scale question answering systems is complicated because training sources usually cover a small portion of the range of possible questions.
Ranked #1 on
Question Answering
on WebQuestions
(F1 metric)
The Goldilocks Principle: Reading Children's Books with Explicit Memory Representations
We introduce a new test of how well language models capture meaning in children's books.
Evaluating Prerequisite Qualities for Learning End-to-End Dialog Systems
A long-term goal of machine learning is to build intelligent conversational agents.
Learning End-to-End Goal-Oriented Dialog
We show similar result patterns on data extracted from an online concierge service.
Key-Value Memory Networks for Directly Reading Documents
Directly reading documents and being able to answer questions from them is an unsolved challenge.
Ranked #12 on
Question Answering
on WikiQA
Tracking the World State with Recurrent Entity Networks
The EntNet sets a new state-of-the-art on the bAbI tasks, and is the first method to solve all the tasks in the 10k training examples setting.
Ranked #5 on
Procedural Text Understanding
on ProPara
Reading Wikipedia to Answer Open-Domain Questions
This paper proposes to tackle open- domain question answering using Wikipedia as the unique knowledge source: the answer to any factoid question is a text span in a Wikipedia article.
Ranked #1 on
Open-Domain Question Answering
on SQuAD1.1
Supervised Learning of Universal Sentence Representations from Natural Language Inference Data
Many modern NLP systems rely on word embeddings, previously trained in an unsupervised manner on large corpora, as base features.
Ranked #1 on
Semantic Textual Similarity
on SentEval
Cross-Lingual Natural Language Inference
Semantic Textual Similarity
+3
ParlAI: A Dialog Research Software Platform
We introduce ParlAI (pronounced "par-lay"), an open-source software platform for dialog research implemented in Python, available at http://parl. ai.
Fader Networks: Manipulating Images by Sliding Attributes
This paper introduces a new encoder-decoder architecture that is trained to reconstruct images by disentangling the salient information of the image and the values of attributes directly in the latent space.
StarSpace: Embed All The Things!
A framework for training and evaluating AI models on a variety of openly available dialogue datasets.
Fader Networks:Manipulating Images by Sliding Attributes
This paper introduces a new encoder-decoder architecture that is trained to reconstruct images by disentangling the salient information of the image and the values of attributes directly in the latent space.
DeSIGN: Design Inspiration from Generative Networks
Can an algorithm create original and compelling fashion designs to serve as an inspirational assistant?
Weaver: Deep Co-Encoding of Questions and Documents for Machine Reading
This paper aims at improving how machines can answer questions directly from text, with the focus of having models that can answer correctly multiple types of questions and from various types of texts, documents or even from large collections of them.
Training Millions of Personalized Dialogue Agents
Current dialogue systems are not very engaging for users, especially when trained end-to-end without relying on proactive reengaging scripted strategies.
Engaging Image Captioning Via Personality
While such tasks are useful to verify that a machine understands the content of an image, they are not engaging to humans as captions.
Image Chat: Engaging Grounded Conversations
To test such models, we collect a dataset of grounded human-human conversations, where speakers are asked to play roles given a provided emotional mood or style, as the use of such traits is also a key factor in engagingness (Guo et al., 2019).
Ranked #2 on
Text Retrieval
on Image-Chat
Learning from Dialogue after Deployment: Feed Yourself, Chatbot!
As our agent engages in conversation, it also estimates user satisfaction in its responses.
Reference-less Quality Estimation of Text Simplification Systems
We show that n-gram-based MT metrics such as BLEU and METEOR correlate the most with human judgment of grammaticality and meaning preservation, whereas simplicity is best evaluated by basic length-based metrics.
Augmenting Transformers with KNN-Based Composite Memory
Various machine learning tasks can benefit from access to external information of different modalities, such as text and images.
Controllable Sentence Simplification
Text simplification aims at making a text easier to read and understand by simplifying grammar and structure while keeping the underlying information identical.
Ranked #3 on
Text Simplification
on ASSET
Using Local Knowledge Graph Construction to Scale Seq2Seq Models to Multi-Document Inputs
Query-based open-domain NLP tasks require information synthesis from long and diverse web results.
Ranked #4 on
Open-Domain Question Answering
on ELI5
Augmenting Transformers with KNN-Based Composite Memory for Dialogue
Various machine learning tasks can benefit from access to external information of different modalities, such as text and images.
ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations
Furthermore, we motivate the need for developing better methods for automatic evaluation using ASSET, since we show that current popular metrics may not be suitable when multiple simplification transformations are performed.
MUSS: Multilingual Unsupervised Sentence Simplification by Mining Paraphrases
Progress in sentence simplification has been hindered by a lack of labeled parallel simplification data, particularly in languages other than English.
Ranked #2 on
Text Simplification
on ASSET
Open-Domain Conversational Agents: Current Progress, Open Problems, and Future Directions
We present our view of what is necessary to build an engaging open-domain conversational agent: covering the qualities of such an agent, the pieces of the puzzle that have been built so far, and the gaping holes we have not filled yet.
Image-Chat: Engaging Grounded Conversations
To test such models, we collect a dataset of grounded human-human conversations, where speakers are asked to play roles given a provided emotional mood or style, as the use of such traits is also a key factor in engagingness (Guo et al., 2019).
Generating Fact Checking Briefs
Fact checking at scale is difficult -- while the number of active fact checking websites is growing, it remains too small for the needs of the contemporary media ecosystem.
