Papers with maximum likelihood estimation
Neural Math Word Problem Solver with Reinforcement Learning (C18-1)
Copied to clipboard
| Challenge: | Existing models for solving math word problems rely on predefined rules or feature engineering. |
| Approach: | They propose to incorporate copy and alignment mechanism into the sequence-to-sequence model to address two shortcomings . they use model output as a feature and incorporate it into the feature-based model to explore the effectiveness . |
| Outcome: | The proposed model outperforms the state-of-the-art models on the problem solving task. |
Translating a Math Word Problem to a Expression Tree (D18-1)
Copied to clipboard
| Challenge: | Sequence-to-sequence (SEQ2SEQ) models have been successfully applied to automatic math word problem solving. |
| Approach: | They propose an equation normalization method to normalize duplicated equations and propose an ensemble model to combine their advantages. |
| Outcome: | The proposed model outperforms the previous state-of-the-art models on the math word problem solving. |
Generative Bridging Network for Neural Sequence Prediction (N18-1)
Copied to clipboard
| Challenge: | Existing approaches to improve the likelihood of sequence prediction models are based on MLE and teacher forcing. |
| Approach: | They propose a Generative Bridging Network (GBN) that extends the point-wise ground truth to a bridge distribution conditioned on it and optimizes their KL-divergence. |
| Outcome: | The proposed bridge module can improve on two recognized sequence prediction tasks and minimize learning burden. |
Generating Temporally-ordered Event Sequences via Event Optimal Transport (2022.coling-1)
Copied to clipboard
| Challenge: | Existing methods for temporal event ordering and event infilling ignore the global semantics of events, and the model adopts a word-level objective to model events in texts. |
| Approach: | They propose a temporal event ordering and event infilling task using a model that uses maximum likelihood estimation to model events in texts. |
| Outcome: | The proposed model outperforms existing models on all evaluation datasets. |
SemRegex: A Semantics-Based Approach for Generating Regular Expressions from Natural Language Specifications (D18-1)
Copied to clipboard
| Challenge: | Existing approaches to generate programs from natural language do not address program aliasing . semantically equivalent programs may have many syntactically different forms . |
| Approach: | They propose a semantics-based approach to generate regular expressions from natural language. |
| Outcome: | The proposed approach improves on three public datasets. |
Token-level and sequence-level loss smoothing for RNN language models (P18-1)
Copied to clipboard
| Challenge: | Maximum likelihood estimation treats all sentences that do not match the ground truth as equally poor, ignoring the structure of the output space. |
| Approach: | They propose to extend the reward augmented maximum likelihood approach to token-level loss smoothing by using token-based approaches to improve the model's performance. |
| Outcome: | The proposed model improves on image captioning and machine translation tasks and treats all sentences that do not match the ground truth as poor . |
BRIO: Bringing Order to Abstractive Summarization (2022.acl-long)
Copied to clipboard
| Challenge: | Abstractive summarization models are often trained with maximum likelihood estimation (MLE) . mLE assumes a deterministic (one-point) target distribution, but can cause performance degradation . |
| Approach: | They propose a new training paradigm which assumes a non-deterministic distribution so that different candidate summaries are assigned probability mass according to their quality. |
| Outcome: | The proposed model can estimate probabilities of candidate summaries that are more correlated with their level of quality. |
Clickbait? Sensational Headline Generation with Auto-tuned Reinforcement Learning (D19-1)
Copied to clipboard
| Challenge: | Conventional abstractive headline generation methods do not optimize for maximum reader attention. |
| Approach: | They propose a model that generates sensational headlines without labeled data by classifying online headlines with many comments against a summarization model. |
| Outcome: | The proposed model generates sensational headlines without labeled data. |
Energy-Based Reranking: Improving Neural Machine Translation Using Energy-Based Models (2021.acl-long)
Copied to clipboard
Sumanta Bhattacharyya, Amirmohammad Rooshenas, Subhajit Naskar, Simeng Sun, Mohit Iyyer, Andrew McCallum
| Challenge: | Autoregressive neural machine translation (NMT) uses a tractable likelihood computation and efficient sampling. |
| Approach: | They propose to use an energy-based model to mimic the behavior of the task measure and use it to train an energy based re-ranking algorithm. |
| Outcome: | The proposed model improves on the samples drawn from the NMT with a higher BLEU score than the experimental model and the energy-based re-ranking algorithm. |
Automatic Poetry Generation with Mutual Reinforcement Learning (D18-1)
Copied to clipboard
| Challenge: | Existing models for automatic poetry generation are based on maximum likelihood estimation (MLE) MLE-based models tend to remember common patterns of the poetry corpus, which results in loss-evaluation mismatch. |
| Approach: | They propose to model the criteria and use them as explicit rewards to guide gradient update by reinforcement learning to motivate the model to pursue higher scores. |
| Outcome: | The proposed model outperforms the current state-of-the-art model and improves on Chinese poetry. |
Is MAP Decoding All You Need? The Inadequacy of the Mode in Neural Machine Translation (2020.coling-main)
Copied to clipboard
| Challenge: | Recent studies have revealed a number of pathologies of neural machine translation systems. |
| Approach: | They propose to use maximum a posteriori decoding to identify the highest-scoring translation, i.e. the mode problem, to validate the model and its training algorithm. |
| Outcome: | The proposed model reproduces the statistical data well, but the beam search strays from the statistics. |
Reinforcement Learning with Large Action Spaces for Neural Machine Translation (2022.coling-1)
Copied to clipboard
| Challenge: | Recent work has argued that the gains produced by Reinforcement learning are mostly due to promoting tokens that have already received a fairly high probability in pre-training. |
| Approach: | They hypothesize that the large action space is a main obstacle to RL’s effectiveness in MT by reducing the size of the vocabulary without changing the vocabulary. |
| Outcome: | The proposed method improves by 1.5 BLEU points on average. |
Beyond BLEU:Training Neural Machine Translation with Semantic Similarity (P19-1)
Copied to clipboard
| Challenge: | Recent work has shown that optimizing neural machine translation systems to directly improve evaluation metrics such as BLEU can improve final translation accuracy. |
| Approach: | They propose a reward function that assigns partial credit to BLEU and provides more diversity in scores than BLUE. |
| Outcome: | The proposed reward function improves translation accuracy, semantic similarity, and human evaluation on four languages trans-lated to English and the optimization procedure converges faster. |
ARAML: A Stable Adversarial Training Framework for Text Generation (D19-1)
Copied to clipboard
| Challenge: | Existing generative adversarial networks suffer from the instability of reinforcement learning training algorithms such as policy gradient, leading to unstable performance. |
| Approach: | They propose a framework where the discriminator assigns rewards to samples acquired from a stationary distribution near the data rather than the generator’s distribution. |
| Outcome: | The proposed framework outperforms state-of-the-art text GANs with a more stable training process. |
End-to-End Synthetic Data Generation for Domain Adaptation of Question Answering Systems (2020.emnlp-main)
Copied to clipboard
Siamak Shakeri, Cicero Nogueira dos Santos, Henghui Zhu, Patrick Ng, Feng Nan, Zhiguo Wang, Ramesh Nallapati, Bing Xiang
| Challenge: | Existing approaches for synthetic QA data generation have limited or no success in improving the downstream Reading Comprehension task. |
| Approach: | They propose an end-to-end approach for synthetic QA data generation using a transformer-based encoder-decoder network that is trained end- to-end to generate both answers and questions. |
| Outcome: | The proposed model outperforms current state-of-the-art methods in the domain adaptation of QA models. |
Diverse Keyphrase Generation with Neural Unlikelihood Training (2020.coling-main)
Copied to clipboard
| Challenge: | Recent advances in neural natural language generation have made possible remarkable progress on the task of keyphrase generation, however, the importance of diversity in keyphrases has been largely ignored. |
| Approach: | They propose to train a sequence-to-sequence keyphrase generation model from the perspective of diversity. |
| Outcome: | The proposed model achieves large diversity gains while maintaining competitive output quality. |
MixCE: Training Autoregressive Language Models by Mixing Forward and Reverse Cross-Entropies (2023.acl-long)
Copied to clipboard
| Challenge: | Autoregressive language models are trained by minimizing the cross-entropy of the model distribution Q relative to the data distribution P. However, these systems still struggle in many openended generation settings, where they are asked to produce a long text following a short prompt. |
| Approach: | They propose to combine forward and reverse cross-entropy to train autoregressive language models by minimizing the cross-Entropy of the model distribution Q relative to the data distribution P. |
| Outcome: | The proposed model overgeneralizes and produces non-human-like text without complex decoding strategies. |
Keyphrase Generation via Soft and Hard Semantic Corrections (2022.emnlp-main)
Copied to clipboard
| Challenge: | Extensive experiments show that CorrKG is capable of generating high-quality keyphrases. |
| Approach: | They propose a correction model CorrKG on top of the MLE pipeline to correct the biases . the adaptive adaptive mass learning scheme is designed to better fit OT and FreqFS . |
| Outcome: | The proposed model overcomes the semantic biases in keyphrase generation using OT and FreqFS techniques. |
Improving Scheduled Sampling with Elastic Weight Consolidation for Neural Machine Translation (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Autoregressive models trained with maximum likelihood estimation suffer from exposure bias, i.e. the discrepancy between ground-truth prefixes used during training and model-generated prefix at inference time. |
| Approach: | They propose to use Elastic Weight Consolidation to better balance mitigating exposure bias with retaining performance. |
| Outcome: | The proposed method significantly outperforms maximum likelihood estimation and scheduled sampling baselines on four translation datasets. |
Focus-Driven Contrastive Learning for Medical Question Summarization (2022.coling-1)
Copied to clipboard
| Challenge: | Existing methods to summarize health questions are not able to capture well question focus and lack the ability to understand sentence-level semantics. |
| Approach: | They propose a question focus-driven contrastive learning framework to capture question focus and exploit contrastive training at both encoder and decoder to obtain better sentence representations. |
| Outcome: | The proposed model achieves 5.33, 12.85 and 3.81 points over the baseline model on three medical benchmark datasets. |
Learning with Instance Bundles for Reading Comprehension (2021.emnlp-main)
Copied to clipboard
| Challenge: | a study shows that training reading comprehension models assumes that the training instances are independent and identically distributed . however, this assumption can cause the learner to ignore distinguishing cues between related or minimally different questions . |
| Approach: | They propose to normalize question-answer scores across neighborhoods of closely contrasting questions and/or answers by adding a cross entropy loss term to the supervision signal. |
| Outcome: | The proposed methods show up to 9% absolute gains in accuracy on two datasets. |
GECSum: Generative Evaluation-Driven Sequence Level Contrastive Learning for Abstractive Summarization (2024.lrec-main)
Copied to clipboard
| Challenge: | Abstractive summarization is a technique in natural language processing that involves generating a summary of a source document by creating new sentences and phrases. |
| Approach: | They propose a sequence-level contrastive learning framework that leverages the semantic understanding capabilities of the abstractive model itself to evaluate summary in reference-based settings. |
| Outcome: | The proposed framework outperforms the state-of-the-art in four summarization datasets. |
Improving Text Generation with Student-Forcing Optimal Transport (2020.emnlp-main)
Copied to clipboard
Jianqiao Li, Chunyuan Li, Guoyin Wang, Hao Fu, Yuhchen Lin, Liqun Chen, Yizhe Zhang, Chenyang Tao, Ruiyi Zhang, Wenlin Wang, Dinghan Shen, Qian Yang, Lawrence Carin
| Challenge: | Maximum likelihood estimation (MLE) is used to train models, but during testing, the model is conditioned on previously generated tokens, resulting in exposure bias. |
| Approach: | They propose to use optimal transport to match the sequences generated in MLE and test modes to reduce exposure bias. |
| Outcome: | The proposed method is validated on machine translation, text summarization, and text generation tasks. |
Prophecy Distillation for Boosting Abstractive Summarization (2024.lrec-main)
Copied to clipboard
| Challenge: | Abstractive summarization models with maximum likelihood estimation generate unfaithful facts alongside ambiguous focus. |
| Approach: | They propose a framework which learns a regular summarization model to mimic the behavior of being guided by prophecy for boosting abstractive summaries. |
| Outcome: | The proposed model achieves new or matched state-of-the-art on four well-known datasets. |
Releasing the Capacity of GANs in Non-Autoregressive Image Captioning (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing non-autoregressive (NAR) models suffer from their inherent multi-modality problem. |
| Approach: | They propose an Adversarial Non-autoregressive Transformer for Image Captioning that improves model performance by modifying model structure to be compatible with contrastive learning. |
| Outcome: | The proposed model achieves 26.72 times faster than the autoregressive model on the MSCOCO dataset. |