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Found 70 papers, 17 papers with code
Using Structured Content Plans for Fine-grained Syntactic Control in Pretrained Language Model Generation
Large pretrained language models offer powerful generation capabilities, but cannot be reliably controlled at a sub-sentential level.
NewsQs: Multi-Source Question Generation for the Inquiring Mind
We present NewsQs (news-cues), a dataset that provides question-answer pairs for multiple news documents.
Characterizing and Measuring Linguistic Dataset Drift
In this paper, we propose three dimensions of linguistic dataset drift: vocabulary, structural, and semantic drift.
Taxonomy Expansion for Named Entity Recognition
We experiment with 6 diverse datasets and show that PLM consistently performs better than most other approaches (0. 5 - 2. 5 F1), including in novel settings for taxonomy expansion not considered in prior work.
A Weak Supervision Approach for Few-Shot Aspect Based Sentiment
We explore how weak supervision on abundant unlabeled data can be leveraged to improve few-shot performance in aspect-based sentiment analysis (ABSA) tasks.
Simple Yet Effective Synthetic Dataset Construction for Unsupervised Opinion Summarization
Opinion summarization provides an important solution for summarizing opinions expressed among a large number of reviews.
Dynamic Benchmarking of Masked Language Models on Temporal Concept Drift with Multiple Views
Temporal concept drift refers to the problem of data changing over time.
Novel Chapter Abstractive Summarization using Spinal Tree Aware Sub-Sentential Content Selection
Summarizing novel chapters is a difficult task due to the input length and the fact that sentences that appear in the desired summaries draw content from multiple places throughout the chapter.
Instruction Tuning for Few-Shot Aspect-Based Sentiment Analysis
Aspect-based Sentiment Analysis (ABSA) is a fine-grained sentiment analysis task which involves four elements from user-generated texts: aspect term, aspect category, opinion term, and sentiment polarity.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+2
Contrastive Training Improves Zero-Shot Classification of Semi-structured Documents
The standard classification setting where categories are fixed during both training and testing falls short in dynamic environments where new document categories could potentially emerge.
Exploring the Role of Task Transferability in Large-Scale Multi-Task Learning
Recent work has found that multi-task training with a large number of diverse tasks can uniformly improve downstream performance on unseen target tasks.
Label Semantics for Few Shot Named Entity Recognition
We study the problem of few shot learning for named entity recognition.
A Bag of Tricks for Dialogue Summarization
Dialogue summarization comes with its own peculiar challenges as opposed to news or scientific articles summarization.
How much pretraining data do language models need to learn syntax?
This calls for a study of the impact of pretraining data size on the knowledge of the models.
Sequential Cross-Document Coreference Resolution
Relating entities and events in text is a key component of natural language understanding.
coreference-resolution
Cross Document Coreference Resolution
+1
On the Evolution of Syntactic Information Encoded by BERT's Contextualized Representations
The adaptation of pretrained language models to solve supervised tasks has become a baseline in NLP, and many recent works have focused on studying how linguistic information is encoded in the pretrained sentence representations.
Event-Driven News Stream Clustering using Entity-Aware Contextual Embeddings
We propose a method for online news stream clustering that is a variant of the non-parametric streaming K-means algorithm.
To BERT or Not to BERT: Comparing Task-specific and Task-agnostic Semi-Supervised Approaches for Sequence Tagging
Leveraging large amounts of unlabeled data using Transformer-like architectures, like BERT, has gained popularity in recent times owing to their effectiveness in learning general representations that can then be further fine-tuned for downstream tasks to much success.
Linking Entities to Unseen Knowledge Bases with Arbitrary Schemas
In entity linking, mentions of named entities in raw text are disambiguated against a knowledge base (KB).
Transition-based Parsing with Stack-Transformers
Modeling the parser state is key to good performance in transition-based parsing.
Ranked #19 on
AMR Parsing
on LDC2017T10
Structural Supervision Improves Few-Shot Learning and Syntactic Generalization in Neural Language Models
Humans can learn structural properties about a word from minimal experience, and deploy their learned syntactic representations uniformly in different grammatical contexts.
Resource-Enhanced Neural Model for Event Argument Extraction
(2) Capturing the long-range dependency, specifically, the connection between an event trigger and a distant event argument.
The appearance of particle tracks in detectors
The phenomenon that a quantum particle propagating in a detector, such as a Wilson cloud chamber, leaves a track close to a classical trajectory is analyzed.
Mathematical Physics Mathematical Physics Quantum Physics
Severing the Edge Between Before and After: Neural Architectures for Temporal Ordering of Events
In this paper, we propose a neural architecture and a set of training methods for ordering events by predicting temporal relations.
Transition-Based Dependency Parsing using Perceptron Learner
Syntactic parsing using dependency structures has become a standard technique in natural language processing with many different parsing models, in particular data-driven models that can be trained on syntactically annotated corpora.
Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learning
Our work involves enriching the Stack-LSTM transition-based AMR parser (Ballesteros and Al-Onaizan, 2017) by augmenting training with Policy Learning and rewarding the Smatch score of sampled graphs.
Ranked #24 on
AMR Parsing
on LDC2017T10
Neural Language Models as Psycholinguistic Subjects: Representations of Syntactic State
We deploy the methods of controlled psycholinguistic experimentation to shed light on the extent to which the behavior of neural network language models reflects incremental representations of syntactic state.
Structural Supervision Improves Learning of Non-Local Grammatical Dependencies
State-of-the-art LSTM language models trained on large corpora learn sequential contingencies in impressive detail and have been shown to acquire a number of non-local grammatical dependencies with some success.
Recursive Subtree Composition in LSTM-Based Dependency Parsing
When ablating the forward LSTM, performance drops less dramatically and composition recovers a substantial part of the gap, indicating that a forward LSTM and composition capture similar information.
IBM Research at the CoNLL 2018 Shared Task on Multilingual Parsing
This paper presents the IBM Research AI submission to the CoNLL 2018 Shared Task on Parsing Universal Dependencies.
Multilingual Neural Machine Translation with Task-Specific Attention
Multilingual machine translation addresses the task of translating between multiple source and target languages.
SemEval 2018 Task 2: Multilingual Emoji Prediction
This paper describes the results of the first Shared Task on Multilingual Emoji Prediction, organized as part of SemEval 2018.
Scheduled Multi-Task Learning: From Syntax to Translation
Neural encoder-decoder models of machine translation have achieved impressive results, while learning linguistic knowledge of both the source and target languages in an implicit end-to-end manner.
Pieces of Eight: 8-bit Neural Machine Translation
Neural machine translation has achieved levels of fluency and adequacy that would have been surprising a short time ago.
Multimodal Emoji Prediction
Emojis are small images that are commonly included in social media text messages.
Towards the Understanding of Gaming Audiences by Modeling Twitch Emotes
Videogame streaming platforms have become a paramount example of noisy user-generated text.
Arc-Standard Spinal Parsing with Stack-LSTMs
We present a neural transition-based parser for spinal trees, a dependency representation of constituent trees.
AMR Parsing using Stack-LSTMs
We present a transition-based AMR parser that directly generates AMR parses from plain text.
Ranked #11 on
AMR Parsing
on LDC2014T12
Greedy Transition-Based Dependency Parsing with Stack LSTMs
During training, dynamic oracles alternate between sampling parser states from the training data and from the model as it is being learned, making the model more robust to the kinds of errors that will be made at test time.
Are Emojis Predictable?
Emojis are ideograms which are naturally combined with plain text to visually complement or condense the meaning of a message.
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.
What Do Recurrent Neural Network Grammars Learn About Syntax?
We investigate what information they learn, from a linguistic perspective, through various ablations to the model and the data, and by augmenting the model with an attention mechanism (GA-RNNG) to enable closer inspection.
Ranked #20 on
Constituency Parsing
on Penn Treebank
Distilling an Ensemble of Greedy Dependency Parsers into One MST Parser
We introduce two first-order graph-based dependency parsers achieving a new state of the art.
Ranked #17 on
Dependency Parsing
on Penn Treebank
Greedy, Joint Syntactic-Semantic Parsing with Stack LSTMs
We present a transition-based parser that jointly produces syntactic and semantic dependencies.
Static and Dynamic Feature Selection in Morphosyntactic Analyzers
Experiments on five languages show that feature selection can result in more compact models as well as higher accuracy under all conditions, but also that a dynamic ordering works better than a static ordering and that joint systems benefit more than standalone taggers.
Training with Exploration Improves a Greedy Stack-LSTM Parser
We adapt the greedy Stack-LSTM dependency parser of Dyer et al. (2015) to support a training-with-exploration procedure using dynamic oracles(Goldberg and Nivre, 2013) instead of cross-entropy minimization.
Ranked #2 on
Chinese Dependency Parsing
on Chinese Pennbank
Neural Architectures for Named Entity Recognition
State-of-the-art named entity recognition systems rely heavily on hand-crafted features and domain-specific knowledge in order to learn effectively from the small, supervised training corpora that are available.
Ranked #8 on
Named Entity Recognition (NER)
on CoNLL++
Recurrent Neural Network Grammars
We introduce recurrent neural network grammars, probabilistic models of sentences with explicit phrase structure.
Ranked #25 on
Constituency Parsing
on Penn Treebank
Many Languages, One Parser
We train one multilingual model for dependency parsing and use it to parse sentences in several languages.
Improved Transition-Based Parsing by Modeling Characters instead of Words with LSTMs
We present extensions to a continuous-state dependency parsing method that makes it applicable to morphologically rich languages.
Transition-Based Dependency Parsing with Stack Long Short-Term Memory
We propose a technique for learning representations of parser states in transition-based dependency parsers.
MaltOptimizer: A System for MaltParser Optimization
Freely available statistical parsers often require careful optimization to produce state-of-the-art results, which can be a non-trivial task especially for application developers who are not interested in parsing research for its own sake.
