Search Results for author:
Found 29 papers, 10 papers with code
Latent Principle Discovery for Language Model Self-Improvement
When language model (LM) users aim to improve the quality of its generations, it is crucial to specify concrete behavioral attributes that the model should strive to reflect.
Insertion Language Models: Sequence Generation with Arbitrary-Position Insertions
Masked Diffusion Models (MDMs) address some of these limitations, but the process of unmasking multiple tokens simultaneously in MDMs can introduce incoherences, and MDMs cannot handle arbitrary infilling constraints when the number of tokens to be filled in is not known in advance.
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.
BRAIn: Bayesian Reward-conditioned Amortized Inference for natural language generation from feedback
Distribution matching methods for language model alignment such as Generation with Distributional Control (GDC) and Distributional Policy Gradient (DPG) have not received the same level of attention in reinforcement learning from human feedback (RLHF) as contrastive methods such as Sequence Likelihood Calibration (SLiC), Direct Preference Optimization (DPO) and its variants.
Scalable Learning of Latent Language Structure With Logical Offline Cycle Consistency
We introduce Logical Offline Cycle Consistency Optimization (LOCCO), a scalable, semi-supervised method for training a neural semantic parser.
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.
Laziness Is a Virtue When It Comes to Compositionality in Neural Semantic Parsing
Nearly all general-purpose neural semantic parsers generate logical forms in a strictly top-down autoregressive fashion.
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.
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.
CBR-iKB: A Case-Based Reasoning Approach for Question Answering over Incomplete Knowledge Bases
Yet, in many question answering applications coupled with knowledge bases, the sparse nature of KBs is often overlooked.
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).
Abstract Meaning Representation
Knowledge Base Question Answering
+1
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.
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)
A Two-Stage Approach towards Generalization in Knowledge Base Question Answering
Most existing approaches for Knowledge Base Question Answering (KBQA) focus on a specific underlying knowledge base either because of inherent assumptions in the approach, or because evaluating it on a different knowledge base requires non-trivial changes.
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)
Generative Relation Linking for Question Answering over Knowledge Bases
Relation linking is essential to enable question answering over knowledge bases.
Ranked #1 on
Relation Linking
on QALD-9
A Semantics-aware Transformer Model of Relation Linking for Knowledge Base Question Answering
Relation linking is a crucial component of Knowledge Base Question Answering systems.
Structural Guidance for Transformer Language Models
Here we study whether structural guidance leads to more human-like systematic linguistic generalization in Transformer language models without resorting to pre-training on very large amounts of data.
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.
Leveraging Abstract Meaning Representation for Knowledge Base Question Answering
Knowledge base question answering (KBQA)is an important task in Natural Language Processing.
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
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
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
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 Part-of-Speech Tagging: Two Unsupervised Approaches
We demonstrate the effectiveness of multilingual learning for unsupervised part-of-speech tagging.
