454 citations · 611 across the 21 of their papers we have counts for
26 papers
BRAIn: Bayesian Reward-conditioned Amortized Inference for natural language generation from feedback
Gaurav Pandey, Yatin Nandwani, Tahira Naseem +6
Distribution matching methods for language model alignment such as Generation with Distributional Control (GDC) and Distributional Policy Gradient (DPG) have not received the same…
Ensemble-Instruct: Generating Instruction-Tuning Data with a Heterogeneous Mixture of LMs
Young-Suk Lee, Md Arafat Sultan, Yousef El-Kurdi +4
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 conversation…
Scalable Learning of Latent Language Structure With Logical Offline Cycle Consistency
Maxwell Crouse, Ramon Astudillo, Tahira Naseem +4
We introduce Logical Offline Cycle Consistency Optimization (LOCCO), a scalable, semi-supervised method for training a neural semantic parser. Conceptually, LOCCO can be viewed as…
Slide, Constrain, Parse, Repeat: Synchronous SlidingWindows for Document AMR Parsing
Sadhana Kumaravel, Tahira Naseem, Ramon Fernandez Astudillo +2
The sliding window approach provides an elegant way to handle contexts of sizes larger than the Transformer's input window, for tasks like language modeling. Here we extend this ap…
Laziness Is a Virtue When It Comes to Compositionality in Neural Semantic Parsing
Maxwell Crouse, Pavan Kapanipathi, Subhajit Chaudhury +4
Nearly all general-purpose neural semantic parsers generate logical forms in a strictly top-down autoregressive fashion. Though such systems have achieved impressive results across…
AMR Parsing with Instruction Fine-tuned Pre-trained Language Models
Young-Suk Lee, Ramón Fernandez Astudillo, Radu Florian +2
Instruction fine-tuned language models on a collection of instruction annotated datasets (FLAN) have shown highly effective to improve model performance and generalization to unsee…
Inducing and Using Alignments for Transition-based AMR Parsing
Andrew Drozdov, Jiawei Zhou, Radu Florian +4
Transition-based parsers for Abstract Meaning Representation (AMR) rely on node-to-word alignments. These alignments are learned separately from parser training and require a compl…
DocAMR: Multi-Sentence AMR Representation and Evaluation
Tahira Naseem, Austin Blodgett, Sadhana Kumaravel +7
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-docum…
Learning to Transpile AMR into SPARQL
Mihaela Bornea, Ramon Fernandez Astudillo, Tahira Naseem +5
We propose a transition-based system to transpile Abstract Meaning Representation (AMR) into SPARQL for Knowledge Base Question Answering (KBQA). This allows us to delegate part of…
Maximum Bayes Smatch Ensemble Distillation for AMR Parsing
Young-Suk Lee, Ramon Fernandez Astudillo, Thanh Lam Hoang +3
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…
Structure-aware Fine-tuning of Sequence-to-sequence Transformers for Transition-based AMR Parsing
Jiawei Zhou, Tahira Naseem, Ramón Fernandez Astudillo +3
Predicting linearized Abstract Meaning Representation (AMR) graphs using pre-trained sequence-to-sequence Transformer models has recently led to large improvements on AMR parsing b…
Ensembling Graph Predictions for AMR Parsing
Hoang Thanh Lam, Gabriele Picco, Yufang Hou +5
In many machine learning tasks, models are trained to predict structure data such as graphs. For example, in natural language processing, it is very common to parse texts into depe…
Structural Guidance for Transformer Language Models
Peng Qian, Tahira Naseem, Roger Levy +1
Transformer-based language models pre-trained on large amounts of text data have proven remarkably successful in learning generic transferable linguistic representations. Here we s…
AMR Parsing with Action-Pointer Transformer
Jiawei Zhou, Tahira Naseem, Ramón Fernandez Astudillo +1
Abstract Meaning Representation parsing is a sentence-to-graph prediction task where target nodes are not explicitly aligned to sentence tokens. However, since graph nodes are sema…
EAT: Enhanced ASR-TTS for Self-supervised Speech Recognition
Murali Karthick Baskar, Lukáš Burget, Shinji Watanabe +2
Self-supervised ASR-TTS models suffer in out-of-domain data conditions. Here we propose an enhanced ASR-TTS (EAT) model that incorporates two main features: 1) The ASR…
Leveraging Abstract Meaning Representation for Knowledge Base Question Answering
Pavan Kapanipathi, Ibrahim Abdelaziz, Srinivas Ravishankar +27
Knowledge base question answering (KBQA)is an important task in Natural Language Processing. Existing approaches face significant challenges including complex question understandin…
Pushing the Limits of AMR Parsing with Self-Learning
Young-Suk Lee, Ramon Fernandez Astudillo, Tahira Naseem +3
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…
Transition-based Parsing with Stack-Transformers
Ramon Fernandez Astudillo, Miguel Ballesteros, Tahira Naseem +2
Modeling the parser state is key to good performance in transition-based parsing. Recurrent Neural Networks considerably improved the performance of transition-based systems by mod…
GPT-too: A language-model-first approach for AMR-to-text generation
Manuel Mager, Ramon Fernandez Astudillo, Tahira Naseem +4
Meaning Representations (AMRs) are broad-coverage sentence-level semantic graphs. Existing approaches to generating text from AMR have focused on training sequence-to-sequence or g…
Semi-supervised Sequence-to-sequence ASR using Unpaired Speech and Text
Murali Karthick Baskar, Shinji Watanabe, Ramon Astudillo +3
Sequence-to-sequence automatic speech recognition (ASR) models require large quantities of data to attain high performance. For this reason, there has been a recent surge in intere…
Cycle-consistency training for end-to-end speech recognition
Takaaki Hori, Ramon Astudillo, Tomoki Hayashi +3
This paper presents a method to train end-to-end automatic speech recognition (ASR) models using unpaired data. Although the end-to-end approach can eliminate the need for expert k…
Back-Translation-Style Data Augmentation for End-to-End ASR
Tomoki Hayashi, Shinji Watanabe, Yu Zhang +4
In this paper we propose a novel data augmentation method for attention-based end-to-end automatic speech recognition (E2E-ASR), utilizing a large amount of text which is not paire…
Expanding Subjective Lexicons for Social Media Mining with Embedding Subspaces
Silvio Amir, Rámon Astudillo, Wang Ling +2
Recent approaches for sentiment lexicon induction have capitalized on pre-trained word embeddings that capture latent semantic properties. However, embeddings obtained by optimizin…
An improved uncertainty decoding scheme with weighted samples for DNN-HMM hybrid systems
Christian Huemmer, Ramón Fernández Astudillo, Walter Kellermann
In this paper, we advance a recently-proposed uncertainty decoding scheme for DNN-HMM (deep neural network - hidden Markov model) hybrid systems. This numerical sampling concept av…