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Found 44 papers, 22 papers with code
A framework for (under)specifying dependency syntax without overloading annotators
We introduce a framework for lightweight dependency syntax annotation.
Augmenting English Adjective Senses with Supersenses
We develop a supersense taxonomy for adjectives, based on that of GermaNet, and apply it to English adjectives in WordNet using human annotation and supervised classification.
"Translation can't change a name": Using Multilingual Data for Named Entity Recognition
Named Entities (NEs) are often written with no orthographic changes across different languages that share a common alphabet.
Learning Word Representations with Hierarchical Sparse Coding
We propose a new method for learning word representations using hierarchical regularization in sparse coding inspired by the linguistic study of word meanings.
Retrofitting Word Vectors to Semantic Lexicons
Vector space word representations are learned from distributional information of words in large corpora.
Multilingual Open Relation Extraction Using Cross-lingual Projection
Open domain relation extraction systems identify relation and argument phrases in a sentence without relying on any underlying schema.
Sparse Overcomplete Word Vector Representations
Current distributed representations of words show little resemblance to theories of lexical semantics.
Non-distributional Word Vector Representations
Data-driven representation learning for words is a technique of central importance in NLP.
Morpho-syntactic Lexicon Generation Using Graph-based Semi-supervised Learning
Morpho-syntactic lexicons provide information about the morphological and syntactic roles of words in a language.
Morphological Inflection Generation Using Character Sequence to Sequence Learning
Morphological inflection generation is the task of generating the inflected form of a given lemma corresponding to a particular linguistic transformation.
Cross-lingual Models of Word Embeddings: An Empirical Comparison
Despite interest in using cross-lingual knowledge to learn word embeddings for various tasks, a systematic comparison of the possible approaches is lacking in the literature.
Problems With Evaluation of Word Embeddings Using Word Similarity Tasks
Our study suggests that the use of word similarity tasks for evaluation of word vectors is not sustainable and calls for further research on evaluation methods.
Learning the Curriculum with Bayesian Optimization for Task-Specific Word Representation Learning
We use Bayesian optimization to learn curricula for word representation learning, optimizing performance on downstream tasks that depend on the learned representations as features.
Polyglot Neural Language Models: A Case Study in Cross-Lingual Phonetic Representation Learning
We introduce polyglot language models, recurrent neural network models trained to predict symbol sequences in many different languages using shared representations of symbols and conditioning on typological information about the language to be predicted.
Correlation-based Intrinsic Evaluation of Word Vector Representations
We introduce QVEC-CCA--an intrinsic evaluation metric for word vector representations based on correlations of learned vectors with features extracted from linguistic resources.
DyNet: The Dynamic Neural Network Toolkit
In the static declaration strategy that is used in toolkits like Theano, CNTK, and TensorFlow, the user first defines a computation graph (a symbolic representation of the computation), and then examples are fed into an engine that executes this computation and computes its derivatives.
CoNLL-SIGMORPHON 2017 Shared Task: Universal Morphological Reinflection in 52 Languages
In sub-task 2, systems were given a lemma and some of its specific inflected forms, and asked to complete the inflectional paradigm by predicting all of the remaining inflected forms.
Cross-Lingual Word Representations: Induction and Evaluation
With the increasing use of monolingual word vectors, there is a need for word vectors that can be used as efficiently across multiple languages as monolingually.
GHH at SemEval-2018 Task 10: Discovering Discriminative Attributes in Distributional Semantics
This paper describes our system submission to the SemEval 2018 Task 10 on Capturing Discriminative Attributes.
Learning To Split and Rephrase From Wikipedia Edit History
Split and rephrase is the task of breaking down a sentence into shorter ones that together convey the same meaning.
Identifying Well-formed Natural Language Questions
Understanding search queries is a hard problem as it involves dealing with "word salad" text ubiquitously issued by users.
Ranked #1 on
Query Wellformedness
on Query Wellformedness
WikiAtomicEdits: A Multilingual Corpus of Wikipedia Edits for Modeling Language and Discourse
We release a corpus of 43 million atomic edits across 8 languages.
UniMorph 2.0: Universal Morphology
The Universal Morphology UniMorph project is a collaborative effort to improve how NLP handles complex morphology across the world's languages.
Text Generation with Exemplar-based Adaptive Decoding
We propose a novel conditioned text generation model.
Handling Divergent Reference Texts when Evaluating Table-to-Text Generation
Automatically constructed datasets for generating text from semi-structured data (tables), such as WikiBio, often contain reference texts that diverge from the information in the corresponding semi-structured data.
Attention Interpretability Across NLP Tasks
The attention layer in a neural network model provides insights into the model's reasoning behind its prediction, which are usually criticized for being opaque.
How to Ask Better Questions? A Large-Scale Multi-Domain Dataset for Rewriting Ill-Formed Questions
We present a large-scale dataset for the task of rewriting an ill-formed natural language question to a well-formed one.
ToTTo: A Controlled Table-To-Text Generation Dataset
We present ToTTo, an open-domain English table-to-text dataset with over 120, 000 training examples that proposes a controlled generation task: given a Wikipedia table and a set of highlighted table cells, produce a one-sentence description.
Ranked #3 on
Data-to-Text Generation
on ToTTo
TIMEDIAL: Temporal Commonsense Reasoning in Dialog
In this paper, we present the first study to investigate pre-trained LMs for their temporal reasoning capabilities in dialogs by introducing a new task and a crowd-sourced English challenge set, TIMEDIAL.
Disfl-QA: A Benchmark Dataset for Understanding Disfluencies in Question Answering
Disfluencies is an under-studied topic in NLP, even though it is ubiquitous in human conversation.
Revisiting the Boundary between ASR and NLU in the Age of Conversational Dialog Systems
As more users across the world are interacting with dialog agents in their daily life, there is a need for better speech understanding that calls for renewed attention to the dynamics between research in automatic speech recognition (ASR) and natural language understanding (NLU).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
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.
Streaming Intended Query Detection using E2E Modeling for Continued Conversation
In voice-enabled applications, a predetermined hotword isusually used to activate a device in order to attend to the query. However, speaking queries followed by a hotword each timeintroduces a cognitive burden in continued conversations.
Efficient Encoders for Streaming Sequence Tagging
A naive application of state-of-the-art bidirectional encoders for streaming sequence tagging would require encoding each token from scratch for each new token in an incremental streaming input (like transcribed speech).
How FaR Are Large Language Models From Agents with Theory-of-Mind?
We propose a new evaluation paradigm for large language models (LLMs): Thinking for Doing (T4D), which requires models to connect inferences about others' mental states to actions in social scenarios.
AutoMix: Automatically Mixing Language Models
Large language models (LLMs) are now available from cloud API providers in various sizes and configurations.
Gemini: A Family of Highly Capable Multimodal Models
This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding.
Ranked #1 on
Multi-task Language Understanding
on MMLU
(using extra training data)
Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention
This work introduces an efficient method to scale Transformer-based Large Language Models (LLMs) to infinitely long inputs with bounded memory and computation.
