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Found 60 papers, 15 papers with code
Predictive Model Selection for Transfer Learning in Sequence Labeling Tasks
Transfer learning is a popular technique to learn a task using less training data and fewer compute resources.
IBM MNLP IE at CASE 2021 Task 2: NLI Reranking for Zero-Shot Text Classification
The NLI reranker uses a textual representation of target types that allows it to score the strength with which a type is implied by a text, without requiring training data for the types.
IBM MNLP IE at CASE 2021 Task 1: Multigranular and Multilingual Event Detection on Protest News
In this paper, we present the event detection models and systems we have developed for Multilingual Protest News Detection - Shared Task 1 at CASE 2021.
CLAPNQ: Cohesive Long-form Answers from Passages in Natural Questions for RAG systems
We present ClapNQ, a benchmark Long-form Question Answering dataset for the full RAG pipeline.
Self-Refinement of Language Models from External Proxy Metrics Feedback
It is often desirable for Large Language Models (LLMs) to capture multiple objectives when providing a response.
Ensemble-Instruct: Generating Instruction-Tuning Data with a Heterogeneous Mixture of LMs
Using in-context learning (ICL) for data generation, techniques such as Self-Instruct (Wang et al., 2023) or the follow-up Alpaca (Taori et al., 2023) can train strong conversational agents with only a small amount of human supervision.
Slide, Constrain, Parse, Repeat: Synchronous SlidingWindows for Document AMR Parsing
We evaluate our oracle and parser using the Abstract Meaning Representation (AMR) parsing 3. 0 corpus.
AMR Parsing with Instruction Fine-tuned Pre-trained Language Models
Instruction fine-tuned language models on a collection of instruction annotated datasets (FLAN) have shown highly effective to improve model performance and generalization to unseen tasks.
UDAPDR: Unsupervised Domain Adaptation via LLM Prompting and Distillation of Rerankers
Many information retrieval tasks require large labeled datasets for fine-tuning.
PrimeQA: The Prime Repository for State-of-the-Art Multilingual Question Answering Research and Development
The field of Question Answering (QA) has made remarkable progress in recent years, thanks to the advent of large pre-trained language models, newer realistic benchmark datasets with leaderboards, and novel algorithms for key components such as retrievers and readers.
Moving Beyond Downstream Task Accuracy for Information Retrieval Benchmarking
Neural information retrieval (IR) systems have progressed rapidly in recent years, in large part due to the release of publicly available benchmarking tasks.
GAAMA 2.0: An Integrated System that Answers Boolean and Extractive Questions
Recent machine reading comprehension datasets include extractive and boolean questions but current approaches do not offer integrated support for answering both question types.
Not to Overfit or Underfit the Source Domains? An Empirical Study of Domain Generalization in Question Answering
Machine learning models are prone to overfitting their training (source) domains, which is commonly believed to be the reason why they falter in novel target domains.
Inducing and Using Alignments for Transition-based AMR Parsing
These alignments are learned separately from parser training and require a complex pipeline of rule-based components, pre-processing, and post-processing to satisfy domain-specific constraints.
Synthetic Target Domain Supervision for Open Retrieval QA
Neural passage retrieval is a new and promising approach in open retrieval question answering.
A Generative Model for Relation Extraction and Classification
Relation extraction (RE) is an important information extraction task which provides essential information to many NLP applications such as knowledge base population and question answering.
DocAMR: Multi-Sentence AMR Representation and Evaluation
Despite extensive research on parsing of English sentences into Abstraction Meaning Representation (AMR) graphs, which are compared to gold graphs via the Smatch metric, full-document parsing into a unified graph representation lacks well-defined representation and evaluation.
Learning to Transpile AMR into SPARQL
We propose a transition-based system to transpile Abstract Meaning Representation (AMR) into SPARQL for Knowledge Base Question Answering (KBQA).
Do Answers to Boolean Questions Need Explanations? Yes
Existing datasets that contain boolean questions, such as BoolQ and TYDI QA , provide the user with a YES/NO response to the question.
Maximum Bayes Smatch Ensemble Distillation for AMR Parsing
AMR parsing has experienced an unprecendented increase in performance in the last three years, due to a mixture of effects including architecture improvements and transfer learning.
Ranked #1 on
AMR Parsing
on LDC2020T02
(using extra training data)
Structure-aware Fine-tuning of Sequence-to-sequence Transformers for Transition-based AMR Parsing
We provide a detailed comparison with recent progress in AMR parsing and show that the proposed parser retains the desirable properties of previous transition-based approaches, while being simpler and reaching the new parsing state of the art for AMR 2. 0, without the need for graph re-categorization.
Ranked #9 on
AMR Parsing
on LDC2017T10
(using extra training data)
VAULT: VAriable Unified Long Text Representation for Machine Reading Comprehension
Existing models on Machine Reading Comprehension (MRC) require complex model architecture for effectively modeling long texts with paragraph representation and classification, thereby making inference computationally inefficient for production use.
AMR Parsing with Action-Pointer Transformer
In this work, we propose a transition-based system that combines hard-attention over sentences with a target-side action pointer mechanism to decouple source tokens from node representations and address alignments.
Ranked #1 on
AMR Parsing
on LDC2014T12
Bootstrapping Multilingual AMR with Contextual Word Alignments
We develop high performance multilingualAbstract Meaning Representation (AMR) sys-tems by projecting English AMR annotationsto other languages with weak supervision.
Multilingual Transfer Learning for QA Using Translation as Data Augmentation
Prior work on multilingual question answering has mostly focused on using large multilingual pre-trained language models (LM) to perform zero-shot language-wise learning: train a QA model on English and test on other languages.
End-to-End QA on COVID-19: Domain Adaptation with Synthetic Training
End-to-end question answering (QA) requires both information retrieval (IR) over a large document collection and machine reading comprehension (MRC) on the retrieved passages.
Towards building a Robust Industry-scale Question Answering System
Building on top of the powerful BERTQA model, GAAMA provides a ∼2. 0{\%} absolute boost in F1 over the industry-scale state-of-the-art (SOTA) system on NQ.
Scalable Cross-lingual Treebank Synthesis for Improved Production Dependency Parsers
We present scalable Universal Dependency (UD) treebank synthesis techniques that exploit advances in language representation modeling which leverage vast amounts of unlabeled general-purpose multilingual text.
A Multilingual Reading Comprehension System for more than 100 Languages
We also show how M-GAAMA can be used in downstream tasks by incorporating it into an END-TO-END-QA system using CFO (Chakravarti et al., 2019).
Pushing the Limits of AMR Parsing with Self-Learning
Abstract Meaning Representation (AMR) parsing has experienced a notable growth in performance in the last two years, due both to the impact of transfer learning and the development of novel architectures specific to AMR.
Ranked #2 on
AMR Parsing
on LDC2014T12
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
Cross-Lingual Relation Extraction with Transformers
Relation extraction (RE) is one of the most important tasks in information extraction, as it provides essential information for many NLP applications.
Multi-Stage Pre-training for Low-Resource Domain Adaptation
Transfer learning techniques are particularly useful in NLP tasks where a sizable amount of high-quality annotated data is difficult to obtain.
ARES: A Reading Comprehension Ensembling Service
We introduce ARES (A Reading Comprehension Ensembling Service): a novel Machine Reading Comprehension (MRC) demonstration system which utilizes an ensemble of models to increase F1 by 2. 3 points.
Cascaded Models for Better Fine-Grained Named Entity Recognition
Named Entity Recognition (NER) is an essential precursor task for many natural language applications, such as relation extraction or event extraction.
Event Presence Prediction Helps Trigger Detection Across Languages
The task of event detection and classification is central to most information retrieval applications.
GPT-too: A language-model-first approach for AMR-to-text generation
Meaning Representations (AMRs) are broad-coverage sentence-level semantic graphs.
Ranked #10 on
AMR-to-Text Generation
on LDC2017T10
Towards Lingua Franca Named Entity Recognition with BERT
In this paper we investigate a single Named Entity Recognition model, based on a multilingual BERT, that is trained jointly on many languages simultaneously, and is able to decode these languages with better accuracy than models trained only on one language.
Ranked #1 on
Cross-Lingual NER
on CoNLL Dutch
The TechQA Dataset
We introduce TechQA, a domain-adaptation question answering dataset for the technical support domain.
Neural Cross-Lingual Relation Extraction Based on Bilingual Word Embedding Mapping
Relation extraction (RE) seeks to detect and classify semantic relationships between entities, which provides useful information for many NLP applications.
Ensembling Strategies for Answering Natural Questions
Many of the top question answering systems today utilize ensembling to improve their performance on tasks such as the Stanford Question Answering Dataset (SQuAD) and Natural Questions (NQ) challenges.
Frustratingly Easy Natural Question Answering
Existing literature on Question Answering (QA) mostly focuses on algorithmic novelty, data augmentation, or increasingly large pre-trained language models like XLNet and RoBERTa.
Ranked #5 on
Question Answering
on Natural Questions (long)
CFO: A Framework for Building Production NLP Systems
This paper introduces a novel orchestration framework, called CFO (COMPUTATION FLOW ORCHESTRATOR), for building, experimenting with, and deploying interactive NLP (Natural Language Processing) and IR (Information Retrieval) systems to production environments.
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
Exploring Graph-structured Passage Representation for Multi-hop Reading Comprehension with Graph Neural Networks
Multi-hop reading comprehension focuses on one type of factoid question, where a system needs to properly integrate multiple pieces of evidence to correctly answer a question.
Ranked #2 on
Question Answering
on COMPLEXQUESTIONS
Neural Cross-Lingual Coreference Resolution and its Application to Entity Linking
We propose an entity-centric neural cross-lingual coreference model that builds on multi-lingual embeddings and language-independent features.
Neural Cross-Lingual Entity Linking
A major challenge in Entity Linking (EL) is making effective use of contextual information to disambiguate mentions to Wikipedia that might refer to different entities in different contexts.
Ranked #3 on
Entity Disambiguation
on TAC2010
One for All: Towards Language Independent Named Entity Linking
Entity linking (EL) is the task of disambiguating mentions in text by associating them with entries in a predefined database of mentions (persons, organizations, etc).
Improving Multilingual Named Entity Recognition with Wikipedia Entity Type Mapping
Experimental results show that the proposed approaches are effective in improving the accuracy of such systems on unseen entities, especially when a system is applied to a new domain or it is trained with little training data (up to 18. 3 F1 score improvement).
Multilingual Named Entity Recognition
named-entity-recognition
+3
Weakly Supervised Cross-Lingual Named Entity Recognition via Effective Annotation and Representation Projection
However, annotating NER data by human is expensive and time-consuming, and can be quite difficult for a new language.
Improving Slot Filling Performance with Attentive Neural Networks on Dependency Structures
Slot Filling (SF) aims to extract the values of certain types of attributes (or slots, such as person:cities\_of\_residence) for a given entity from a large collection of source documents.
Reinforcement Learning for Transition-Based Mention Detection
This paper describes an application of reinforcement learning to the mention detection task.
Bilateral Multi-Perspective Matching for Natural Language Sentences
Natural language sentence matching is a fundamental technology for a variety of tasks.
Ranked #17 on
Paraphrase Identification
on Quora Question Pairs
(Accuracy metric)
Multi-Perspective Context Matching for Machine Comprehension
Based on this dataset, we propose a Multi-Perspective Context Matching (MPCM) model, which is an end-to-end system that directly predicts the answer beginning and ending points in a passage.
Ranked #3 on
Open-Domain Question Answering
on SQuAD1.1
A Sentence Compression Based Framework to Query-Focused Multi-Document Summarization
We consider the problem of using sentence compression techniques to facilitate query-focused multi-document summarization.
Toward Mention Detection Robustness with Recurrent Neural Networks
One of the key challenges in natural language processing (NLP) is to yield good performance across application domains and languages.
A Joint Model for Answer Sentence Ranking and Answer Extraction
Answer sentence ranking and answer extraction are two key challenges in question answering that have traditionally been treated in isolation, i. e., as independent tasks.
