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Found 141 papers, 62 papers with code
Multidomain Pretrained Language Models for Green NLP
Thus, instead of training multiple models, we can train a single multidomain model saving on computational resources and training time.
Few-Shot Text Generation with Natural Language Instructions
Providing pretrained language models with simple task descriptions in natural language enables them to solve some tasks in a fully unsupervised fashion.
Wine is not v i n. On the Compatibility of Tokenizations across Languages
The size of the vocabulary is a central design choice in large pretrained language models, with respect to both performance and memory requirements.
Analyzing Hate Speech Data along Racial, Gender and Intersectional Axes
To tackle the rising phenomenon of hate speech, efforts have been made towards data curation and analysis.
Flow-Adapter Architecture for Unsupervised Machine Translation
The primary novelties of our model are: (a) capturing language-specific sentence representations separately for each language using normalizing flows and (b) using a simple transformation of these latent representations for translating from one language to another.
Domain Adaptation for Sparse-Data Settings: What Do We Gain by Not Using Bert?
The practical success of much of NLP depends on the availability of training data.
CaMEL: Case Marker Extraction without Labels
We introduce CaMEL (Case Marker Extraction without Labels), a novel and challenging task in computational morphology that is especially relevant for low-resource languages.
Enhanced Temporal Knowledge Embeddings with Contextualized Language Representations
We align structured knowledge contained in temporal knowledge graphs with their textual descriptions extracted from news articles and propose a novel knowledge-text prediction task to inject the abundant information from descriptions into temporal knowledge embeddings.
Knowledge Graph Completion
Temporal Knowledge Graph Completion
Graph Neural Networks for Multiparallel Word Alignment
First, we create a multiparallel word alignment graph, joining all bilingual word alignment pairs in one graph.
Geographic Adaptation of Pretrained Language Models
Geographic linguistic features are commonly used to improve the performance of pretrained language models (PLMs) on NLP tasks where geographic knowledge is intuitively beneficial (e. g., geolocation prediction and dialect feature prediction).
Modular and Parameter-Efficient Multimodal Fusion with Prompting
Recent research has made impressive progress in large-scale multimodal pre-training.
CoDA21: Evaluating Language Understanding Capabilities of NLP Models With Context-Definition Alignment
Pretrained language models (PLMs) have achieved superhuman performance on many benchmarks, creating a need for harder tasks.
Semantic-Oriented Unlabeled Priming for Large-Scale Language Models
Due to the high costs associated with finetuning large language models, various recent works propose to adapt them to specific tasks without any parameter updates through in-context learning.
Towards a Broad Coverage Named Entity Resource: A Data-Efficient Approach for Many Diverse Languages
Prior work on extracting MNE datasets from parallel corpora required resources such as large monolingual corpora or word aligners that are unavailable or perform poorly for underresourced languages.
LMTurk: Few-Shot Learners as Crowdsourcing Workers in a Language-Model-as-a-Service Framework
We propose LMTurk, a novel approach that treats few-shot learners as crowdsourcing workers.
True Few-Shot Learning with Prompts -- A Real-World Perspective
Prompt-based approaches are strong at few-shot learning.
BeliefBank: Adding Memory to a Pre-Trained Language Model for a Systematic Notion of Belief
We show that, in a controlled experimental setting, these two mechanisms result in more consistent beliefs in the overall system, improving both the accuracy and consistency of its answers over time.
Active Learning for Argument Mining: A Practical Approach
Despite considerable recent progress, the creation of well-balanced and diverse resources remains a time-consuming and costly challenge in Argument Mining.
Scene Graph Generation for Better Image Captioning?
We investigate the incorporation of visual relationships into the task of supervised image caption generation by proposing a model that leverages detected objects and auto-generated visual relationships to describe images in natural language.
BERT Cannot Align Characters
We show that the closer two languages are, the better BERT can align them on the character level.
Locating Language-Specific Information in Contextualized Embeddings
Multilingual pretrained language models (MPLMs) exhibit multilinguality and are well suited for transfer across languages.
Wine is Not v i n. -- On the Compatibility of Tokenizations Across Languages
The size of the vocabulary is a central design choice in large pretrained language models, with respect to both performance and memory requirements.
Graph Algorithms for Multiparallel Word Alignment
With the advent of end-to-end deep learning approaches in machine translation, interest in word alignments initially decreased; however, they have again become a focus of research more recently.
Continuous Entailment Patterns for Lexical Inference in Context
If we allow for tokens outside the PLM's vocabulary, patterns can be adapted more flexibly to a PLM's idiosyncrasies.
Ranked #1 on
Few-Shot NLI
on SherLIiC
Discrete and Soft Prompting for Multilingual Models
It has been shown for English that discrete and soft prompting perform strongly in few-shot learning with pretrained language models (PLMs).
ParCourE: A Parallel Corpus Explorer for a Massively Multilingual Corpus
With more than 7000 languages worldwide, multilingual natural language processing (NLP) is essential both from an academic and commercial perspective.
Data Centric Domain Adaptation for Historical Text with OCR Errors
We propose new methods for in-domain and cross-domain Named Entity Recognition (NER) on historical data for Dutch and French.
Modeling Ideological Agenda Setting and Framing in Polarized Online Groups with Graph Neural Networks and Structured Sparsity
The increasing polarization of online political discourse calls for computational tools that are able to automatically detect and monitor ideological divides in social media.
Multi-source Neural Topic Modeling in Multi-view Embedding Spaces
Though word embeddings and topics are complementary representations, several past works have only used pretrained word embeddings in (neural) topic modeling to address data sparsity in short-text or small collection of documents.
Generating Datasets with Pretrained Language Models
To obtain high-quality sentence embeddings from pretrained language models (PLMs), they must either be augmented with additional pretraining objectives or finetuned on a large set of labeled text pairs.
Ranked #5 on
Semantic Textual Similarity
on SICK
Static Embeddings as Efficient Knowledge Bases?
Recent research investigates factual knowledge stored in large pretrained language models (PLMs).
Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP
In this paper, we first demonstrate a surprising finding: pretrained language models recognize, to a considerable degree, their undesirable biases and the toxicity of the content they produce.
Position Information in Transformers: An Overview
Transformers are arguably the main workhorse in recent Natural Language Processing research.
Language Models for Lexical Inference in Context
Lexical inference in context (LIiC) is the task of recognizing textual entailment between two very similar sentences, i. e., sentences that only differ in one expression.
Ranked #2 on
Few-Shot NLI
on SherLIiC
Improving Scene Graph Classification by Exploiting Knowledge from Texts
We show that by fine-tuning the classification pipeline with the extracted knowledge from texts, we can achieve ~8x more accurate results in scene graph classification, ~3x in object classification, and ~1. 5x in predicate classification, compared to the supervised baselines with only 1% of the annotated images.
Does He Wink or Does He Nod? A Challenging Benchmark for Evaluating Word Understanding of Language Models
Recent progress in pretraining language models on large corpora has resulted in large performance gains on many NLP tasks.
Multilingual LAMA: Investigating Knowledge in Multilingual Pretrained Language Models
(i) Can mBERT be used as a multilingual knowledge base?
Measuring and Improving Consistency in Pretrained Language Models
In this paper we study the question: Are Pretrained Language Models (PLMs) consistent with respect to factual knowledge?
Superbizarre Is Not Superb: Derivational Morphology Improves BERT's Interpretation of Complex Words
How does the input segmentation of pretrained language models (PLMs) affect their interpretations of complex words?
A Closer Look at Few-Shot Crosslingual Transfer: The Choice of Shots Matters
Few-shot crosslingual transfer has been shown to outperform its zero-shot counterpart with pretrained encoders like multilingual BERT.
Few-Shot Text Generation with Pattern-Exploiting Training
Providing pretrained language models with simple task descriptions in natural language enables them to solve some tasks in a fully unsupervised fashion.
Subword Sampling for Low Resource Word Alignment
This method's hypothesis is that the aggregation of different granularities of text for certain language pairs can help word-level alignment.
Automatically Identifying Words That Can Serve as Labels for Few-Shot Text Classification
A recent approach for few-shot text classification is to convert textual inputs to cloze questions that contain some form of task description, process them with a pretrained language model and map the predicted words to labels.
Dynamic Contextualized Word Embeddings
Static word embeddings that represent words by a single vector cannot capture the variability of word meaning in different linguistic and extralinguistic contexts.
TopicBERT for Energy Efficient Document Classification
Our work thus focuses on optimizing the computational cost of fine-tuning for document classification.
It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners
When scaled to hundreds of billions of parameters, pretrained language models such as GPT-3 (Brown et al., 2020) achieve remarkable few-shot performance.
Investigating Pretrained Language Models for Graph-to-Text Generation
We show that the PLMs BART and T5 achieve new state-of-the-art results and that task-adaptive pretraining strategies improve their performance even further.
Ranked #1 on
KG-to-Text Generation
on WebNLG (All)
Automatic Domain Adaptation Outperforms Manual Domain Adaptation for Predicting Financial Outcomes
In our experiments, we demonstrate that the automatically adapted sentiment dictionary outperforms the previous state of the art in predicting the financial outcomes excess return and volatility.
Neural Topic Modeling with Continual Lifelong Learning
To address the problem, we propose a lifelong learning framework for neural topic modeling that can continuously process streams of document collections, accumulate topics and guide future topic modeling tasks by knowledge transfer from several sources to better deal with the sparse data.
Explainable and Discourse Topic-aware Neural Language Understanding
Marrying topic models and language models exposes language understanding to a broader source of document-level context beyond sentences via topics.
Are Pretrained Language Models Symbolic Reasoners Over Knowledge?
How can pretrained language models (PLMs) learn factual knowledge from the training set?
Modeling Graph Structure via Relative Position for Text Generation from Knowledge Graphs
We present Graformer, a novel Transformer-based encoder-decoder architecture for graph-to-text generation.
Ranked #5 on
KG-to-Text Generation
on AGENDA
Unsupervised Embedding-based Detection of Lexical Semantic Changes
This paper describes EmbLexChange, a system introduced by the "Life-Language" team for SemEval-2020 Task 1, on unsupervised detection of lexical-semantic changes.
BERT-kNN: Adding a kNN Search Component to Pretrained Language Models for Better QA
Khandelwal et al. (2020) use a k-nearest-neighbor (kNN) component to improve language model performance.
DagoBERT: Generating Derivational Morphology with a Pretrained Language Model
Can pretrained language models (PLMs) generate derivationally complex words?
Identifying Necessary Elements for BERT's Multilinguality
We aim to identify architectural properties of BERT and linguistic properties of languages that are necessary for BERT to become multilingual.
Masking as an Efficient Alternative to Finetuning for Pretrained Language Models
We present an efficient method of utilizing pretrained language models, where we learn selective binary masks for pretrained weights in lieu of modifying them through finetuning.
Quantifying the Contextualization of Word Representations with Semantic Class Probing
Pretrained language models have achieved a new state of the art on many NLP tasks, but there are still many open questions about how and why they work so well.
SimAlign: High Quality Word Alignments without Parallel Training Data using Static and Contextualized Embeddings
We find that alignments created from embeddings are superior for four and comparable for two language pairs compared to those produced by traditional statistical aligners, even with abundant parallel data; e. g., contextualized embeddings achieve a word alignment F1 for English-German that is 5 percentage points higher than eflomal, a high-quality statistical aligner, trained on 100k parallel sentences.
Inexpensive Domain Adaptation of Pretrained Language Models: Case Studies on Biomedical NER and Covid-19 QA
Domain adaptation of Pretrained Language Models (PTLMs) is typically achieved by unsupervised pretraining on target-domain text.
Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference
Some NLP tasks can be solved in a fully unsupervised fashion by providing a pretrained language model with "task descriptions" in natural language (e. g., Radford et al., 2019).
Multipurpose Intelligent Process Automation via Conversational Assistant
Such intelligent agents can assist the user by answering specific questions and executing routine tasks that are ordinarily performed in a natural language (i. e., customer support).
Extending Machine Language Models toward Human-Level Language Understanding
We take language to be a part of a system for understanding and communicating about situations.
Morphological Segmentation Inside-Out
Morphological segmentation has traditionally been modeled with non-hierarchical models, which yield flat segmentations as output.
Sentence Meta-Embeddings for Unsupervised Semantic Textual Similarity
We address the task of unsupervised Semantic Textual Similarity (STS) by ensembling diverse pre-trained sentence encoders into sentence meta-embeddings.
E-BERT: Efficient-Yet-Effective Entity Embeddings for BERT
We present a novel way of injecting factual knowledge about entities into the pretrained BERT model (Devlin et al., 2019): We align Wikipedia2Vec entity vectors (Yamada et al., 2016) with BERT's native wordpiece vector space and use the aligned entity vectors as if they were wordpiece vectors.
Negated and Misprimed Probes for Pretrained Language Models: Birds Can Talk, But Cannot Fly
We find that PLMs do not distinguish between negated ("Birds cannot [MASK]") and non-negated ("Birds can [MASK]") cloze questions.
BERTRAM: Improved Word Embeddings Have Big Impact on Contextualized Model Performance
In this work, we transfer this idea to pretrained language models: We introduce BERTRAM, a powerful architecture based on BERT that is capable of inferring high-quality embeddings for rare words that are suitable as input representations for deep language models.
Linguistically Informed Relation Extraction and Neural Architectures for Nested Named Entity Recognition in BioNLP-OST 2019
Our RE system ranked first in the SeeDev-binary Relation Extraction Task with F1-score of 0. 3738.
Type-aware Convolutional Neural Networks for Slot Filling
In particular, we explore different ways of integrating the named entity types of the relation arguments into a neural network for relation classification, including a joint training and a structured prediction approach.
BioNLP-OST 2019 RDoC Tasks: Multi-grain Neural Relevance Ranking Using Topics and Attention Based Query-Document-Sentence Interactions
This paper presents our system details and results of participation in the RDoC Tasks of BioNLP-OST 2019.
Generating Multi-Sentence Abstractive Summaries of Interleaved Texts
The interleaved posts are encoded hierarchically, i. e., word-to-word (words in a post) followed by post-to-post (posts in a channel).
Multi-source Multi-view Transfer Learning in Neural Topic Modeling with Pretrained Topic and Word Embeddings
Though word embeddings and topics are complementary representations, several past works have only used pretrained word embeddings in (neural) topic modeling to address data sparsity problem in short text or small collection of documents.
Multi-view and Multi-source Transfers in Neural Topic Modeling with Pretrained Topic and Word Embeddings
Though word embeddings and topics are complementary representations, several past works have only used pre-trained word embeddings in (neural) topic modeling to address data sparsity problem in short text or small collection of documents.
Neural Architectures for Fine-Grained Propaganda Detection in News
To address the tasks of sentence (SLC) and fragment level (FLC) propaganda detection, we explore different neural architectures (e. g., CNN, LSTM-CRF and BERT) and extract linguistic (e. g., part-of-speech, named entity, readability, sentiment, emotion, etc.
Probing for Semantic Classes: Diagnosing the Meaning Content of Word Embeddings
Word embeddings typically represent different meanings of a word in a single conflated vector.
A Hierarchical Decoder with Three-level Hierarchical Attention to Generate Abstractive Summaries of Interleaved Texts
Interleaved texts, where posts belonging to different threads occur in one sequence, are a common occurrence, e. g., online chat conversations.
SherLIiC: A Typed Event-Focused Lexical Inference Benchmark for Evaluating Natural Language Inference
We present SherLIiC, a testbed for lexical inference in context (LIiC), consisting of 3985 manually annotated inference rule candidates (InfCands), accompanied by (i) ~960k unlabeled InfCands, and (ii) ~190k typed textual relations between Freebase entities extracted from the large entity-linked corpus ClueWeb09.
Ranked #1 on
Lexical Entailment
on SherLIiC
Fine-Grained Argument Unit Recognition and Classification
In this work, we argue that the task should be performed on a more fine-grained level of sequence labeling.
An Unsupervised Joint System for Text Generation from Knowledge Graphs and Semantic Parsing
To this end, we present the first approach to unsupervised text generation from KGs and show simultaneously how it can be used for unsupervised semantic parsing.
Ranked #1 on
Unsupervised KG-to-Text Generation
on VG graph-text
Analytical Methods for Interpretable Ultradense Word Embeddings
In this work, we investigate three methods for making word spaces interpretable by rotation: Densifier (Rothe et al., 2016), linear SVMs and DensRay, a new method we propose.
Rare Words: A Major Problem for Contextualized Embeddings And How to Fix it by Attentive Mimicking
Pretraining deep neural network architectures with a language modeling objective has brought large improvements for many natural language processing tasks.
Attentive Mimicking: Better Word Embeddings by Attending to Informative Contexts
Learning high-quality embeddings for rare words is a hard problem because of sparse context information.
Learning Semantic Representations for Novel Words: Leveraging Both Form and Context
The general problem setting is that word embeddings are induced on an unlabeled training corpus and then a model is trained that embeds novel words into this induced embedding space.
CIS at TAC Cold Start 2015: Neural Networks and Coreference Resolution for Slot Filling
Especially, it focuses on the coreference and classification component.
Multilingual Embeddings Jointly Induced from Contexts and Concepts: Simple, Strong and Scalable
A simple and effective context-based multilingual embedding learner is Levy et al. (2017)'s S-ID (sentence ID) method.
Aligning Very Small Parallel Corpora Using Cross-Lingual Word Embeddings and a Monogamy Objective
Count-based word alignment methods, such as the IBM models or fast-align, struggle on very small parallel corpora.
Multi-Multi-View Learning: Multilingual and Multi-Representation Entity Typing
For representation, we consider representations based on the context distribution of the entity (i. e., on its embedding), on the entity's name (i. e., on its surface form) and on its description in Wikipedia.
Neural Relation Extraction Within and Across Sentence Boundaries
iDepNN models the shortest and augmented dependency paths via recurrent and recursive neural networks to extract relationships within (intra-) and across (inter-) sentence boundaries.
Ranked #1 on
Relation Extraction
on MUC6
textTOvec: Deep Contextualized Neural Autoregressive Topic Models of Language with Distributed Compositional Prior
We address two challenges of probabilistic topic modelling in order to better estimate the probability of a word in a given context, i. e., P(word|context): (1) No Language Structure in Context: Probabilistic topic models ignore word order by summarizing a given context as a "bag-of-word" and consequently the semantics of words in the context is lost.
Neural Transductive Learning and Beyond: Morphological Generation in the Minimal-Resource Setting
Neural state-of-the-art sequence-to-sequence (seq2seq) models often do not perform well for small training sets.
Interpretable Textual Neuron Representations for NLP
Input optimization methods, such as Google Deep Dream, create interpretable representations of neurons for computer vision DNNs.
Document Informed Neural Autoregressive Topic Models with Distributional Prior
Here, we extend a neural autoregressive topic model to exploit the full context information around words in a document in a language modeling fashion.
Neural Semi-Markov Conditional Random Fields for Robust Character-Based Part-of-Speech Tagging
Character-level models of tokens have been shown to be effective at dealing with within-token noise and out-of-vocabulary words.
Document Informed Neural Autoregressive Topic Models
Context information around words helps in determining their actual meaning, for example "networks" used in contexts of artificial neural networks or biological neuron networks.
LISA: Explaining Recurrent Neural Network Judgments via Layer-wIse Semantic Accumulation and Example to Pattern Transformation
Recurrent neural networks (RNNs) are temporal networks and cumulative in nature that have shown promising results in various natural language processing tasks.
News Article Teaser Tweets and How to Generate Them
In this work, we define the task of teaser generation and provide an evaluation benchmark and baseline systems for the process of generating teasers.
Evaluating Word Embeddings in Multi-label Classification Using Fine-grained Name Typing
We propose a new evaluation method for word embeddings based on multi-label classification given a word embedding.
Replicated Siamese LSTM in Ticketing System for Similarity Learning and Retrieval in Asymmetric Texts
The task is challenging due to significant term mismatch in the query and ticket pairs of asymmetric lengths, where subject is a short text but description and solution are multi-sentence texts.
Recurrent One-Hop Predictions for Reasoning over Knowledge Graphs
Large scale knowledge graphs (KGs) such as Freebase are generally incomplete.
Joint Bootstrapping Machines for High Confidence Relation Extraction
Semi-supervised bootstrapping techniques for relationship extraction from text iteratively expand a set of initial seed instances.
End-Task Oriented Textual Entailment via Deep Explorations of Inter-Sentence Interactions
This work deals with SciTail, a natural entailment challenge derived from a multi-choice question answering problem.
Fortification of Neural Morphological Segmentation Models for Polysynthetic Minimal-Resource Languages
Morphological segmentation for polysynthetic languages is challenging, because a word may consist of many individual morphemes and training data can be extremely scarce.
Neural Architectures for Open-Type Relation Argument Extraction
In this work, we introduce the task of Open-Type Relation Argument Extraction (ORAE): Given a corpus, a query entity Q and a knowledge base relation (e. g.,"Q authored notable work with title X"), the model has to extract an argument of non-standard entity type (entities that cannot be extracted by a standard named entity tagger, e. g. X: the title of a book or a work of art) from the corpus.
Embedding Learning Through Multilingual Concept Induction
We present a new method for estimating vector space representations of words: embedding learning by concept induction.
Evaluating neural network explanation methods using hybrid documents and morphological agreement
The behavior of deep neural networks (DNNs) is hard to understand.
Deep Temporal-Recurrent-Replicated-Softmax for Topical Trends over Time
We also introduce a metric (named as SPAN) to quantify the capability of dynamic topic model to capture word evolution in topics over time.
Impact of Coreference Resolution on Slot Filling
In this paper, we demonstrate the importance of coreference resolution for natural language processing on the example of the TAC Slot Filling shared task.
Attentive Convolution: Equipping CNNs with RNN-style Attention Mechanisms
We hypothesize that this is because the attention in CNNs has been mainly implemented as attentive pooling (i. e., it is applied to pooling) rather than as attentive convolution (i. e., it is integrated into convolution).
Corpus-level Fine-grained Entity Typing
This paper addresses the problem of corpus-level entity typing, i. e., inferring from a large corpus that an entity is a member of a class such as "food" or "artist".
Global Normalization of Convolutional Neural Networks for Joint Entity and Relation Classification
We introduce globally normalized convolutional neural networks for joint entity classification and relation extraction.
Unlabeled Data for Morphological Generation With Character-Based Sequence-to-Sequence Models
We present a semi-supervised way of training a character-based encoder-decoder recurrent neural network for morphological reinflection, the task of generating one inflected word form from another.
Past, Present, Future: A Computational Investigation of the Typology of Tense in 1000 Languages
We present SuperPivot, an analysis method for low-resource languages that occur in a superparallel corpus, i. e., in a corpus that contains an order of magnitude more languages than parallel corpora currently in use.
One-Shot Neural Cross-Lingual Transfer for Paradigm Completion
We present a novel cross-lingual transfer method for paradigm completion, the task of mapping a lemma to its inflected forms, using a neural encoder-decoder model, the state of the art for the monolingual task.
Comparative Study of CNN and RNN for Natural Language Processing
Deep neural networks (DNN) have revolutionized the field of natural language processing (NLP).
Task-Specific Attentive Pooling of Phrase Alignments Contributes to Sentence Matching
This work studies comparatively two typical sentence matching tasks: textual entailment (TE) and answer selection (AS), observing that weaker phrase alignments are more critical in TE, while stronger phrase alignments deserve more attention in AS.
Multi-level Representations for Fine-Grained Typing of Knowledge Base Entities
Entities are essential elements of natural language.
Joint Semantic Synthesis and Morphological Analysis of the Derived Word
Since morphology obeys the principle of compositionality, the semantics of the word can be systematically derived from the meaning of its parts.
Noise Mitigation for Neural Entity Typing and Relation Extraction
For the second noise type, we propose ways to improve the integration of noisy entity type predictions into relation extraction.
Exploring Different Dimensions of Attention for Uncertainty Detection
Neural networks with attention have proven effective for many natural language processing tasks.
Neural Multi-Source Morphological Reinflection
We explore the task of multi-source morphological reinflection, which generalizes the standard, single-source version.
Intrinsic Subspace Evaluation of Word Embedding Representations
We introduce a new methodology for intrinsic evaluation of word representations.
Corpus-level Fine-grained Entity Typing Using Contextual Information
This paper addresses the problem of corpus-level entity typing, i. e., inferring from a large corpus that an entity is a member of a class such as "food" or "artist".
Simple Question Answering by Attentive Convolutional Neural Network
In fact selection, we match the subject entity in a fact candidate with the entity mention in the question by a character-level convolutional neural network (char-CNN), and match the predicate in that fact with the question by a word-level CNN (word-CNN).
Single-Model Encoder-Decoder with Explicit Morphological Representation for Reinflection
Morphological reinflection is the task of generating a target form given a source form, a source tag and a target tag.
Combining Recurrent and Convolutional Neural Networks for Relation Classification
This paper investigates two different neural architectures for the task of relation classification: convolutional neural networks and recurrent neural networks.
Why and How to Pay Different Attention to Phrase Alignments of Different Intensities
We address the problems of identifying phrase alignments of flexible granularity and pooling alignments of different intensities for these tasks.
Discriminative Phrase Embedding for Paraphrase Identification
This work, concerning paraphrase identification task, on one hand contributes to expanding deep learning embeddings to include continuous and discontinuous linguistic phrases.
Online Updating of Word Representations for Part-of-Speech Tagging
We propose online unsupervised domain adaptation (DA), which is performed incrementally as data comes in and is applicable when batch DA is not possible.
Comparing Convolutional Neural Networks to Traditional Models for Slot Filling
We address relation classification in the context of slot filling, the task of finding and evaluating fillers like "Steve Jobs" for the slot X in "X founded Apple".
Multichannel Variable-Size Convolution for Sentence Classification
We propose MVCNN, a convolution neural network (CNN) architecture for sentence classification.
Ultradense Word Embeddings by Orthogonal Transformation
Embeddings are generic representations that are useful for many NLP tasks.
Attention-Based Convolutional Neural Network for Machine Comprehension
Understanding open-domain text is one of the primary challenges in natural language processing (NLP).
ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs
(ii) We propose three attention schemes that integrate mutual influence between sentences into CNN; thus, the representation of each sentence takes into consideration its counterpart.
Learning Meta-Embeddings by Using Ensembles of Embedding Sets
Word embeddings -- distributed representations of words -- in deep learning are beneficial for many tasks in natural language processing (NLP).
AutoExtend: Extending Word Embeddings to Embeddings for Synsets and Lexemes
We present \textit{AutoExtend}, a system to learn embeddings for synsets and lexemes.
Distributional Models and Deep Learning Embeddings: Combining the Best of Both Worlds
There are two main approaches to the distributed representation of words: low-dimensional deep learning embeddings and high-dimensional distributional models, in which each dimension corresponds to a context word.
Deep Learning Embeddings for Discontinuous Linguistic Units
Deep learning embeddings have been successfully used for many natural language processing problems.
