# Modeling Extractive Question Answering Using Encoder-Decoder Models with Constrained Decoding and Evaluation-Based Reinforcement Learning

^{*}

## Abstract

**:**

## 1. Introduction

- 1.
- We solve the extractive QA task with an encoder-decoder model that generates all answer words jointly, enabling the model to use more information from the answers for training and to naturally output entire answers in the inference.
- 2.
- The proposed encoder-decoder extractive QA model uses evaluation-based reinforcement learning to enhance the model’s performance. The experiment results show that the proposed model can achieve better results than the baseline.

## 2. Background and Related Work

#### 2.1. Extractive Question Answering

#### 2.1.1. Independent Assumption for the Start and End Positions

#### 2.1.2. Greedy Search in the Multistep Decomposition

#### 2.1.3. Neural Network Models Used for Prediction

#### 2.2. Encoder-Decoder Models

#### 2.3. Reinforcement Learning for Encoder-Decoder Models

## 3. Methods

#### 3.1. Modeling the Whole Answer Span Using the Encoder-Decoder Model

#### 3.2. Constrained Decoding

Algorithm 1: Constrained Decoding. | |

Input: Question Q and Document D, Vocabulary $\mathcal{V}$Input: The $\mathrm{decoder}$, Trie tree T and its functions: $\mathrm{add}(T,\dots )$, $\mathrm{search}(T,\dots )$Output: Answer $\tilde{A}$ | |

1: $\tilde{A}\leftarrow \left\{\right\}$, $T\leftarrow \mathrm{\Phi}$, $i\leftarrow 0$, ${\tilde{y}}_{0}\leftarrow <start>$ | |

2: for $k\leftarrow 1$ to $\left|D\right|$ do | ▷ Initialize the trie tree T |

3: $\mathrm{add}(T,\{{d}_{k},{d}_{k+1},\dots ,{d}_{\left|D\right|}\})$ | ▷ Add a substring that starts with ${d}_{k}$ into trie tree T. |

4: end for | |

5: while ${\tilde{y}}_{i}$≠ <$end$> do | |

6: ${\mathcal{V}}_{\mathrm{c}}\leftarrow \{<end>\}$ | ▷ Initialize the constrained vocabulary |

7: $\mathcal{P}\leftarrow \mathrm{search}(T,\tilde{A})$ | ▷ Obtain the substring starting with $\tilde{A}$ |

8: foreach $\{{p}_{1},{p}_{2},\dots \}\in \mathcal{P}$ do | ▷ Loop over each substring in D starting with $\tilde{A}$ |

9: $P=\{{p}_{1},{p}_{2},\dots \}-\tilde{A}$ | ▷ Remove the prefix $\tilde{A}$ from substring $\{{p}_{1},{p}_{2},\dots \}$ |

10: ${\mathcal{V}}_{\mathrm{c}}\leftarrow {\mathcal{V}}_{\mathrm{c}}+{P}_{\left[1\right]}$ | ▷ Add the first token ${P}_{\left[1\right]}$ in P into ${\mathcal{V}}_{\mathrm{c}}$ |

11: end for | |

12: ${\tilde{y}}_{i}=\underset{w\in {\mathcal{V}}_{\mathrm{c}}}{\mathrm{argmax}}\left(\mathrm{decoder}\left(w\mid {\tilde{y}}_{1},{\tilde{y}}_{2},\dots ,{\tilde{y}}_{i-1}\right)\right).$ | |

13: $\tilde{A}\leftarrow \tilde{A}+{\tilde{y}}_{i}$ | ▷ Save the predicted words |

14: end while | |

15: return $\tilde{A}$ |

#### 3.3. Evaluation-Based Reinforcement Learning

## 4. Results and Discussion

#### 4.1. Experiment Settings

#### 4.2. Main Results

- 1.
**BiDAF [12]:**a classical extractive QA model that uses bidirectional attention flow (question-to-document and document-to-question attention) to enrich the representation of words. BiDAF predicts the answers’ start and end positions independently according to the representations.- 2.
- 3.
**DCN [63]:**locates the answer spans by iteratively predicting the start and end positions to overcome the initial local maxima, which may lead to the wrong answers.- 4.
**DCN+ [50]:**introduces reinforcement learning techniques to optimize the F1 metric for extractive QA directly.- 5.
**R.M-Reader [51]:**a memory-based model that uses reinforcement learning with a reward function refined for better coverage.- 6.
**BERT-****base****[8]:**an extractive QA model based on a powerful pretrained language model. We downloaded the model from https://huggingface.co/csarron/bert-base-uncased-squad-v1/tree/main (accessed on 20 February 2023) and evaluated it locally.- 7.
**BERT-****base****\w compound (best) [14]:**jointly predicts the start and end positions. It is similar to Model 2:**BiDAF\w compound (best)**.- 8.
**BART-base:**directly trains a BART-base model to generate the whole answer based on the question and answer.

No. | Model | EM | F1 | #Out of Document |
---|---|---|---|---|

1 | BiDAF [12] | 66.16 | 76.19 | 0 |

2 | \w compound (best) [14] | 66.96 | 75.90 | 0 |

3 | DCN [63] | 65.4 | 75.6 | 0 |

4 | DCN+ [50] | 74.5 | 83.1 | 0 |

5 | R.M-Reader [51] | 78.9 | 86.3 | 0 |

6 | BERT-base [8] | 80.92 | 88.24 | 0 |

7 | \w compound (best) [14] | 81.83 | 88.52 | 0 |

8 | BART-base | 78.10 | 87.17 | 410 |

9 | BART-base\w Constrained | 79.80 | 88.05 | 0 |

10 | RL\w EM&F1 | 78.37 | 87.87 | 329 |

11 | RL\w EM&F1 Constrained | 79.84 | 88.39 | 0 |

12 | RL\w F1 | 78.83 | 88.04 | 310 |

13 | RL\w F1 Constrained | 80.02 | 88.54 | 0 |

14 | RL\w ROUGE-L | 78.27 | 87.44 | 304 |

15 | RL\w ROUGE-L Constrained | 79.39 | 87.97 | 0 |

#### 4.3. Case Study and Discussion

## 5. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 1.**An extractive question-answering system takes a question and a document as the input and extracts a span from the document as the output answer.

**Figure 2.**An encoder-decoder model autoregressively generates an output word sequence based on the input. <$start$> and <$end$> are the special tokens representing the generation’s start and end.

**Figure 3.**The comparison between the proposed model and a baseline model. The input question is “Which NFL team represented the NFC at Super Bowl 50?”. The answer is “Santa Clara California”. (

**a**) The encoder-decoder model is used to solve the extractive QA task, taking advantage of all words in the answer. (

**b**) The baseline extractive QA models use the start and end words only.

**Figure 4.**The average length of the answers predicted by the models. (

**a**) The numbers of words in the answers. (

**b**) The numbers of characters in the answers.

**Figure 5.**The F1 scores of the predictions as the answer gets longer. (

**a**) Grouped by numbers of words. (

**b**) Grouped by numbers of characters.

**Table 1.**Samples from the SQuAD dataset. An extractive QA model needs to understand the natural language question and the evidence in the document to find the answer span from the document ${}^{*}$.

NO. | Question | Document | Answer |
---|---|---|---|

1 | Which NFL team represented the AFC at Super Bowl 50? | Super Bowl 50 was an American football game to determine the champion of the National Football League (NFL) for the 2015 season. The American Football Conference (AFC) champion Denver Broncos defeated the National Football Conference (NFC) champion Carolina Panthers 24-10 to earn their third Super Bowl title... | Denver Broncos |

2 | Who was in charge of the papal army in the War of Barbastro? | The legendary religious zeal of the Normans was exercised in religious wars long before the First Crusade carved out a Norman principality in Antioch. They were major foreign participants in the Reconquista in Iberia. In 1018, Roger de Tosny traveled to the Iberian Peninsula to carve out a state for himself from Moorish lands, but he failed. In 1064, during the War of Barbastro, William of Montreuil led the papal army... | William of Montreuil |

Hyperparameter | Value | Description |
---|---|---|

Batch size | 32 | Number of Samples in each Batch |

Learning Rate (LR) | $5\times {10}^{-5}$ | Coefficient for updating the parameters |

LR scheduler | Linear warmup | Tune the LR as the training step increases ${}^{1}$ |

LR warmup steps | 500 | The parameter for LR scheduler |

Optimizer | AdamW | Adamw optimizer provided by Pytorch ${}^{2}$ |

Weight Decay | 0.01 | Coefficient for scaling the parameters down |

Betas | 0.9, 0.999 | Coefficients used for computing running averages of gradient and its square ${}^{3}$ |

k | 4 | Sampled sequences in Equation (14) |

beam size | 4 | The number of beams for the beam search |

^{1}https://huggingface.co/docs/transformers/v4.26.1/en/main_classes/optimizer_schedules#transformers.get_linear_schedule_with_warmup, accessed on 20 February 2023.

^{2}https://pytorch.org/, accessed on 20 February 2023.

^{3}https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html, accessed on 20 February 2023.

Model | EM | F1 | Beam Size |
---|---|---|---|

BART-base | 77.83 | 87.15 | 1 |

78.10 | 87.17 | 4 | |

77.98 | 87.18 | 16 | |

BART-base RL\w EM&F1 | 77.92 | 87.51 | 1 |

78.37 | 87.87 | 4 | |

78.09 | 87.81 | 16 | |

BART-base RL\w F1 | 78.43 | 87.77 | 1 |

78.83 | 88.04 | 4 | |

78.61 | 88.01 | 16 | |

BART-base RL\w ROUGE-L | 77.77 | 87.08 | 1 |

78.27 | 87.44 | 4 | |

78.14 | 87.41 | 16 |

**Table 5.**A case study for BERT-base (Baseline) and BART-base RL\w F1 (Our Model) on SQuAD dataset ${}^{*}$.

NO. | Question | Document | Predictions |
---|---|---|---|

1 | Who did the Normans team up with in Anatolia? | Some Normans joined Turkish forces to aid in the destruction of the Armenians vassal-states of Sassoun and Taron in far eastern Anatolia. Later, many took up service with... | Baseline: Armenians |

Our Model: Turkish forces | |||

2 | What month, day, and year did the Super Bowl 50 take place? | Super Bowl 50 was an American football game used to determine the champion of the National Football League... The game was played on 7 February 2016 at Leviś Stadium in the... | Baseline: February |

Our Model: 7 February 2016 |

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## Share and Cite

**MDPI and ACS Style**

Li, S.; Sun, C.; Liu, B.; Liu, Y.; Ji, Z.
Modeling Extractive Question Answering Using Encoder-Decoder Models with Constrained Decoding and Evaluation-Based Reinforcement Learning. *Mathematics* **2023**, *11*, 1624.
https://doi.org/10.3390/math11071624

**AMA Style**

Li S, Sun C, Liu B, Liu Y, Ji Z.
Modeling Extractive Question Answering Using Encoder-Decoder Models with Constrained Decoding and Evaluation-Based Reinforcement Learning. *Mathematics*. 2023; 11(7):1624.
https://doi.org/10.3390/math11071624

**Chicago/Turabian Style**

Li, Shaobo, Chengjie Sun, Bingquan Liu, Yuanchao Liu, and Zhenzhou Ji.
2023. "Modeling Extractive Question Answering Using Encoder-Decoder Models with Constrained Decoding and Evaluation-Based Reinforcement Learning" *Mathematics* 11, no. 7: 1624.
https://doi.org/10.3390/math11071624