Neural image-to-text radiology report generation systems offer the potential to improve radiology reporting by reducing the repetitive process of report drafting and identifying possible medical errors. However, existing report generation systems, despite achieving high performances on natural language generation metrics such as CIDEr or BLEU, still suffer from incomplete and inconsistent generations. Here we introduce two new simple rewards to encourage the generation of factually complete and consistent radiology reports: one that encourages the system to generate radiology domain entities consistent with the reference, and one that uses natural language inference to encourage these entities to be described in inferentially consistent ways. We combine these with the novel use of an existing semantic equivalence metric (BERTScore). We further propose a report generation system that optimizes these rewards via reinforcement learning. On two open radiology report datasets, our system substantially improved the F 1 score of a clinical information extraction performance by +22 . 1 (  + 63 . 9% ). We further show via a human evaluation and a qualitative analysis that our system leads to generations that are more factually complete and consistent compared to the baselines. An important new application of natural language generation (NLG) is to build assistive systems that take X-ray images of a patient and generate a textual report describing clinical observations in the images (Jing et al., 2018; Li et al., 2018; Liu et al., 2019; Boag et al., 2020; Chen et al., 2020). Figure 1 shows an example of a radiology report generated by such a system. This is a clinically important task, offering the potential to reduce radiologists' repetitive work and generally improve clinical communication (Kahn et al., 2009). Automatic radiology report generation systems have achieved promising performance as measured by widely used NLG metrics such as CIDEr (Vedantam et al., 2015) and BLEU (Papineni et al., 2002) on several datasets (Li et al., 2018; Jing et al., 2019; Chen et al., 2020). However, reports that achieve high performance on these NLG metrics are not always factually complete or consistent. In addition to the use of inadequate metrics, the factual incompleteness and inconsistency issue in generated reports is further exacerbated by the inadequate training of these systems. Specifically, the standard teacher-forcing training algorithm (Williams and Zipser, 1989) used by most existing work can lead to a discrepancy between what the model sees during training and test time (Ran-zato et al., 2016), resulting in degenerate outputs with factual hallucinations (Maynez et al., 2020). Liu et al. (2019) and Boag et al. (2020) have shown that reports generated by state-of-the-art systems still have poor quality when evaluated by their clinical metrics as measured with an information extraction system designed for radiology reports. For example, the generated report in Figure 1 is incomplete since it neglects an observation of atelectasis that can be found in the images. It is also inconsistent since it mentions left-sided pleural effusion which is not present in the images. Indeed, we show that existing systems are inadequate in factual completeness and consistency, and that an image-to-text radiology report generation system can be substantially improved by replacing widely used NLG metrics with simple alternatives. We propose two new simple rewards that can encourage the factual completeness and consistency of the generated reports. First, we propose the Exact Entity Match Reward ( fact ENT ) which captures the completeness of a generated report by measuring its coverage of entities in the radiology domain, compared with a reference report. The goal of the reward is to better capture disease and anatomical knowledge that are encoded in the entities. Second, we propose the Entailing Entity Match Reward ( fact ENTNLI ), which extends fact ENT with a natural language inference (NLI) model that further considers how inferentially consistent the generated entities are with their descriptions in the reference. We add NLI to control the overestimation of disease when optimizing towards fact ENT . We use these two metrics along with an existing semantic equivalence metric, BERTScore (Zhang et al., 2020a), to potentially capture synonyms (e.g., left and right effusions are synonymous with bilat-eral effusions) and distant dependencies between diseases (e.g., a negation like . . . but underlying consolidation or other pulmonary lesion not excluded) that are present in radiology reports. Although recent work in summarization, dialogue, and data-to-text generation has tried to address this problem of factual incompleteness and inconsistency by using natural language inference (NLI) (Falke et al., 2019; Welleck et al., 2019), question answering (QA) (Wang et al., 2020a), or content matching constraint (Wang et al., 2020b) approaches, they either show negative results or are not directly applicable to the generation of radiology reports due to a substantial task and domain difference. To construct the NLI model for fact ENTNLI , we present a weakly supervised approach that adapts an existing NLI model to the radiology domain. We further present a report generation model which directly optimizes a Transformer-based architecture with these rewards using reinforcement learning (RL). We evaluate our proposed report generation model on two publicly available radiology report generation datasets. We find that optimizing the proposed rewards along with BERTScore by RL leads to generated reports that achieve substantially improved performance in the important clinical metrics (Liu et al., 2019; Boag et al., 2020; Chen et al., 2020), demonstrating the higher clinical value of our approach. We make all our code and the expert-labeled test set for evaluating the radiology NLI model publicly available to encourage future research 1 . To summarize, our contributions in this paper are: 1. We propose two simple rewards for image-to-text radiology report generation, which focus on capturing the factual completeness and consistency of generated reports, and a weak supervision-based approach for training a radiology-domain NLI model to realize the second reward. 2. We present a new radiology report generation model that directly optimizes these new rewards with RL, showing that previous approaches that optimize traditional NLG metrics are inadequate, and that the proposed approach substantially improves performance on clinical metrics (as much as  + 64 . 2% ) on two publicly available datasets. Wang et al. (2018) and Jing et al. (2018) first proposed multi-task learning models that jointly generate a report and classify disease labels from a chest X-ray image. Their models were extended to use multiple images (Yuan et al., 2019), to adopt a hybrid retrieval-generation model (Li et al., 2018), or to consider structure information (Jing et al., 2019). More recent work has focused on generating reports that are clinically consistent and accurate. Liu et al. (2019) presented a system that generates accurate reports by fine-tuning it with their Clinically 1 https://github.com/ysmiura/ifcc Coherent Reward. Boag et al. (2020) evaluated several baseline generation systems with clinical metrics and found that standard NLG metrics are ill-equipped for this task. Very recently, Chen et al. (2020) proposed an approach to generate radiology reports with a memory-driven Transformer. Our work is most related to Liu et al. (2019); their system, however, is dependent on a rule-based information extraction system specifically created for chest X-ray reports and has limited robustness and generalizability to different domains within radiology. By contrast, we aim to develop methods that improve the factual completeness and consistency of generated reports by harnessing more robust statistical models and are easily generalizable. A variety of recent work has focused on consistency and faithfulness in generation. Our work is inspired by Falke et al. (2019), Welleck et al. (2019), and Matsumaru et al. (2020) in using NLI to rerank or filter generations in text summarization, dialogue, and headline generations systems, respectively. Other attempts in this direction include evaluating consistency in generations using QA models (Durmus et al., 2020; Wang et al., 2020a; Maynez et al., 2020), with distantly supervised classifiers (Kryscinski et al., 2020), and with task-specific content matching constraints (Wang et al., 2020b). Liu et al. (2019) and Zhang et al. (2020b) studied improving the factual correctness in generating radiology reports with rule-based information extraction systems. Our work mainly differs from theirs in the direct optimization of factual completeness with an entity-based reward and of factual consistency with a statistical NLI-based reward. The problem of generating text from image data has been widely studied in the image captioning setting. While early work focused on combining convolutional neural network (CNN) and recurrent neural network (RNN) architectures (Vinyals et al., 2015), more recent work has discovered the effectiveness of using the Transformer architecture (Vaswani et al., 2017). Li et al. (2019) and Pan et al. (2020) introduced an attention process to exploit semantic and visual information into this architecture. Herdade et al. (2019), Cornia et al. (2020), and Guo et al. (2020) extended this architecture to learn geometrical and other relationships between input Memory-Augmented Attention Add & Norm Feed Forward Add & Norm MaskedSelf-Attention Add & Norm Feed Forward Add & Norm Cross-Attention Add & Norm   # ! regions. We find Meshed-Memory Transformer (Cornia et al., 2020) ( M 2 Trans) to be more effective in our radiology report generation task than the traditional RNN-based models and Transformer models (an empirical result will be shown in 4), and therefore use it as our base architecture. Formally, given K individual images x 1 ...K of a patient, our task involves generating a sequence of words to form a textual report  y , which describes the clinical observations in the images. This task resembles image captioning, except with multiple images as input and longer text sequences as output. We therefore extend a state-of-the-art image captioning model, M 2 Trans (Cornia et al., 2020), with multi-image input as our base architecture. We first briefly introduce this model and refer interested readers to Cornia et al. (2020). Figure 2 illustrates an overview of the M 2 Trans model. Given an image x k , image regions are first extracted with a CNN as X = CNN( x k ) . X is then encoded with a memory-augmented attention process M mem ( X ) as M mem ( X ) = Att(W q X , K , V ) (1) Att( Q , K , V ) = softmax (cid:18) QKT  d (cid:19) V (2) K = [ W k X ; M k ] (3) V = [ W v X ; M v ] (4) where W q , W k , W v are weights, M k , M v are memory matrices, d is a scaling factor, and [  ;  ] is the concatenation operation. Att( Q , K , V ) is an attention process derived from the Transformer architecture (Vaswani et al., 2017) and extended to include memory matrices that can encode a priori knowledge between image regions. In the encoder, this attention process is a self-attention process since all of the query Q , the key K , and the value V depend on X . M mem ( X ) is further processed with a feed forward layer, a residual connection, and a layer normalization to output  X . This encoding process can be stacked N times and is applied to K images, and n -th layer output of K image will be  X n, K . The meshed decoder first processes an encoded text Y with a masked self-attention and further processes it with a feed forward layer, a residual connection, and a layer normalization to output  Y . Y is then passed to a cross attention C (  X n, K ,  Y ) and a meshed attention M mesh (  XN , K ,  Y ) as M mesh (  XN , K ,  Y ) = (cid:88) n  n (cid:12) C (  X n, K ,  Y ) (5) C (  X n, K ,  Y ) = max K (Att(W q  Y , W k  X n , K , W v  X n , K )) (6)  n =  (cid:16) W n [ Y ; C (  X n, K ,  Y )] + b n (cid:17) (7) where (cid:12) is element-wise multiplication, max K is max-pooling over K images,  is sigmoid function, W n is a weight, and b n is a bias. The weighted summation in M mesh (  XN , K ,  Y ) exploits both low-level and high-level information from the N stacked encoder. Differing from the self-attention process in the encoder, the cross attention uses a query that depends on Y and a key and a value that depend on X . M mesh (  XN , K ,  Y ) is further processed with a feed forward layer, a residual connection, and a layer normalization to output  Y . As like in the encoder, the decoder can be stacked N times to output  YN . YN is further passed to a feed forward layer to output report  y . We designed an F-score entity match reward to capture factual completeness. This reward assumes that entities encode disease and anatomical knowledge that relates to factual completeness. A named entity recognizer is applied to  y and the corresponding reference report y . Given entities E gen and E ref recognized from y gen and y ref respectively, precision (pr) and recall (rc) of entity match are calculated as pr ENT = (cid:80) e  E gen  ( e, E ref ) | E gen | (8) rc ENT = (cid:80) e  E ref  ( e, E gen ) | E ref | (9)  ( e, E ) = (cid:40) 1 , for e  E 0 , otherwise (10) The harmonic mean of precision and recall is taken as fact ENT to reward a balanced match of entities. We used Stanza (Qi et al., 2020) and its clinical models (Zhang et al., 2020c) as a named entity recognizer for radiology reports. For example in the case of Figure 1, the common entities among the reference report and the generated report are pleural and effusion , resulting to fact ENT = 33 . 3 . We additionally designed an F-score style reward that expands fact ENT with NLI to capture factual consistency. NLI is used to control the overestimation of disease when optimizing towards fact ENT . In fact ENTNLI ,  in Eq. 10 is expanded to  ( e,E )=   1 , for e  E  NLI e ( P , h ) (cid:54) = contradiction 1 , for NLI e ( P , h ) = entailment 0 , otherwise (11) NLI e ( P , h ) = nli ( p, h ) where  p = arg max p  P sim ( h, p ) (12) where h is a sentence that includes e , P is all sentences in a counter part text (if h is a sentence in a generated report, P is all sentences in the corresponding reference report), nli (  ,  ) is an NLI function that returns an NLI label which is one of { entailment , neutral , contradiction } , and sim (  ,  ) is a text similarity function. We used BERTScore (Zhang et al., 2020a) as sim (  ,  ) in the experiments (the detail of BERTScore can be found in Appendix A). The harmonic mean of precision and recall is taken as fact ENTNLI to encourage a balanced factual consistency between a generated text and the corresponding reference text. For example in the case of Figure 1, the sentence The left-sided pleural effusion has increased in size and is now moderate in size. will be contradictory to There is no left pleural effusion. resulting in pleural and effusion being rejected in y gen . We integrate the proposed factual rewards into self-critical sequence training (Rennie et al., 2017). An RL loss LRL is minimized as the negative expectation of the reward r . The gradient of the loss is estimated with a single Monte Carlo sample as   LRL (  ) =   log P  ( y sp | x 1 ...K ) ( r ( y sp )  r ( y gd )) (13) where  y sp is a sampled text and  y gd is a greedy decoded text. Paulus et al. (2018) and Zhang et al. (2020b) have shown that a generation can be improved by combining multiple losses. We combine a factual metric loss with a language model loss and an NLG loss as L =  1 LNLL +  2 LRL _ NLG +  3 LRL _ FACT (14) where LNLL is a language model loss, LRL _ NLG is the RL loss using an NLG metric (e.g., CIDEr or BERTScore), LRL _ FACT is the RL loss using a factual reward (e.g., fact ENT or fact ENTNLI ), and   are scaling factors to balance the multiple losses. We propose a weakly-supervised approach to construct an NLI model for radiology reports. (There already exists an NLI system for the medical domain, MedNLI (Romanov and Shivade, 2018), but we found that a model trained on MedNLI does not work well on radiology reports.) Given a large scale dataset of radiology reports, a sentence pair is sampled and filtered with weakly-supervised rules. The rules are prepared to extract a randomly sampled sentence pair ( s 1 and s 2 ) that are in an entailment, neutral, or contradiction relation. We designed the following 6 rules for weak-supervision. Neutral 4 (N4) (1) NE of s 1 are equal to NE of s 2 and (2) s 1 and s 2 include observation keywords. The rules rely on a semantic similarity measure and the overlap of entities to determine the relationship between s 1 and s 2 . In the neutral rules and the contradiction rule, we included similarity measures to avoid extracting easy to distinguish sentence pairs. We evaluated this NLI by preparing training data, validation data, and test data. For the training data, the training set of MIMIC-CXR (Johnson et al., 2019) is used as the source of sentence pairs. 2 k pairs are extracted for E1 and C1, 0 . 5 k pairs are extracted for N1, N2, N3, and N4, resulting in a total of 6 k pairs. The training set of MedNLI is also used as additional data. For the validation data and the test data, we sampled 480 sentence pairs from the validation section of MIMIC-CXR and had them annotated by two experts: one medical expert and one NLP expert. Each pair is annotated twice swapping its premise and hypothesis resulting in 960 pairs and are split in half resulting in 480 pair for a validation set and 480 pairs for a test set. The test set of MedNLI is also used as alternative test data. We used BERT (Devlin et al., 2019) as an NLI model since it performed as a strong baseline in the existing MedNLI system (Ben Abacha et al., 2019), and used Stanza (Qi et al., 2020) and its clinical models (Zhang et al., 2020c) as a named entity recognizer. Table 1 shows the result of the model trained with and without the weakly-supervised data. The accuracy of NLI on radiology data increased substantially by +24 . 5 % with the addition of the radiology NLI training set. (See Appendix A for the detail of the rules, the datasets, and the model configuration.) 4 Experiments 4.1 Data We used the training and validation sets of MIMIC-CXR (Johnson et al., 2019) to train and validate models. MIMIC-CXR is a large publicly available database of chest radiographs. We extracted the findings sections from the reports with a text extraction tool for MIMIC-CXR 2 , and used them as our reference reports as in previous work (Liu et al., 2019; Boag et al., 2020). Findings section is a natural language description of the important aspects in a radiology image. The reports with empty findings sections were discarded, resulting in 152173 and 1196 reports for the training and validation set, respectively. We used the test set of MIMIC-CXR and the entire Open-i Chest X-ray dataset (Demner-Fushman et al., 2012) as two individual test sets. Open-i is another publicly available database of chest radiographs which has been widely used in past studies. We again extracted the findings sections, resulting in 2347 reports for MIMIC-CXR and 3335 reports for Open-i. Open-i is used only for testing since the number of reports is too small to train and test a neural report generation model. BLEU4, CIDEr-D & BERTScore: We first use general NLG metrics to evaluate the generation quality. These metrics include the 4-gram BLEU scroe (Papineni et al., 2002, BLEU4), CIDEr score (Vedantam et al., 2015) with gaming penalties (CIDEr-D), and the F 1 score of the BERTScore (Zhang et al., 2020a). Clinical Metrics: However, NLG metrics such as BLEU and CIDEr are known to be inadequate for evaluating factual completeness and consistency. We therefore followed previous work (Liu et al., 2019; Boag et al., 2020; Chen et al., 2020) by additionally evaluating the clinical accuracy of the generated reports using a clinical information extraction system. We use CheXbert (Smit et al., 2020), an information extraction system for chest reports, to extract the presence status of a series of observations (i.e., whether a disease is present or not), and score a generation by comparing the values of these observations to those obtained from 2 https://github.com/MIT-LCP/mimic-cxr/tree/master/txt the reference 3 . The micro average of accuracy, precision, recall, and F 1 scores are calculated over 5 observations (following previous work (Irvin et al., 2019)) for: atelectasis , cardiomegaly , consolidation , edema , and pleural effusion 4 . fact ENT & fact ENTNLI : We additionally include our proposed rewards fact ENT and fact ENTNLI as metrics to compare their values for different models. We used M 2 Trans as our report generation model and used DenseNet-121 (Huang et al., 2017) as our image encoder. We trained M 2 Trans with the following variety of joint losses. NLL+CDr CIDEr-D and NLL loss is jointly optimized with  1 = 0 . 01 and  2 = 0 . 99 for the scaling factors. NLL+BS The F 1 score of BERTScore and NLL loss is jointly optimized with  1 = 0 . 01 and  2 = 0 . 99 . NLL+BS+ fc E fact ENT is added to NLL+BS with  1 = 0 . 01 ,  2 = 0 . 495 , and  3 = 0 . 495 . NLL+BS+ fc EN fact ENTNLI is added to NLL+BS with  1 = 0 . 01 ,  2 = 0 . 495 , and  3 = 0 . 495 . We additionally prepared three previous models that have been tested on MIMIC-CXR. TieNet We reimplemented the model of Wang et al. (2018) consisting of a CNN encoder and an RNN decoder optimized with a multitask setting of language generation and image classification. CNN RNN 2 We reimplemented the model of Liu et al. (2019) consisting of a CNN encoder and a hierarchical RNN decoder optimized with CIDEr and Clinically Coherent Reward 3 We used CheXbert instead of CheXpert (Irvin et al., 2019) since CheXbert was evaluated to be approximately 5 . 5% more accurate than CheXpert. The evaluation using CheXpert can be found in Appendix C. 4 These 5 observations are evaluated to be most represented in real-world radiology reports and therefore using these 5 observations (and excluding others) leads to less variance and more statistical strength in the results. We include the detailed results of the clinical metrics in Appendix C for completeness. R2Gen The model of Chen et al. (2020) with a CNN encoder and a memory-driven Transformer optimized with NLL loss. We used the publicly available official code and its checkpoint as its implementation. Table 2 shows the results of the baselines 5 and M 2 Trans optimized with the five different joint losses. We find that the best result for a metric or a reward is achieved when that metric or reward is used directly in the optimization objective. Notably, for the proposed factual rewards, the increases of +3 . 6 fact ENT and +4 . 9 fact ENTNLI are observed 5 These MIMIC-CXR scores have some gaps from the previously reported values with some possible reasons. First, TieNet and CNN RNN 2 in Liu et al. (2019) are evaluated on a pre-release version of MIMIC-CXR. Second, we used report-level evaluation for all models, but Chen et al. (2020) tested R2Gen using image-level evaluation. on MIMIC-CXR with M 2 Trans when compared against M 2 Trans w/ BS. For the clinical metrics, the best recalls and F 1 scores are obtained with M 2 Trans using fact ENT as a reward, achieving a substantial +22 . 1 increase ( +63 . 9% ) in F 1 score against the best baseline R2Gen. We further find that using fact ENTNLI as a reward leads to higher precision and accuracy compared to fact ENT with decreases in the recalls. The best precisions and accuracies were obtained in the baseline CNN RNN 2 . This is not surprising since this model directly optimizes the clinical metrics with its Clinically Coherent Reward. However, this model is strongly optimized against precision resulting in the low recalls and F 1 scores. The results of M 2 Trans without the proposed rewards and BERTScore reveal the strength of M 2 Trans and the inadequacy of NLL loss and CIDEr for factual completeness and consistency. M 2 Trans w/ NLL shows strong improvements in the clinical metrics against R2Gen. These improvements are a little surprising since both models are Transformer-based models and are optimized with NLL loss. We assume that these improvements are due to architecture differences such as memory matrices in the encoder of M 2 Trans. The differ-M 2 Trans w/ BS R2Gen No Proposed (simple) (Chen et al., 2020) difference 36 . ence between NLL and NLL+CDr on M 2 Trans indicates that NLL and CIDEr are unreliable for factual completeness and consistency. We performed a human evaluation to further con-firm whether the generated radiology reports are factually complete and consistent. Following prior studies of radiology report summarization (Zhang et al., 2020b) and image captioning evaluation (Vedantam et al., 2015), we designed a simple human evaluation task. Given a reference report (R) and two candidate model generated reports (C1, C2), two board-certified radiologists decided whether C1 or C2 is more factually similar to R. To consider cases when C1 and C2 are difficult to differentiate, we also prepared No difference as an answer. We sampled 100 reports randomly from the test set of MIMIC-CXR for this evaluation. Since this evaluation is (financially) expensive and there has been no human evaluation between the baseline models, we selected R2Gen as the best previous model and M 2 Trans w/ BS as the most simple proposed model, in order to be able to weakly infer that all of our proposed models are better than all of the baselines. Table 3 shows the result of the evaluation. The majority of the reports were labeled No difference but the proposed approach received three times as much preference as the baseline. There are two main reasons why No difference was frequent in human evaluation. First, we found that a substantial portion of the examples were normal studies (no abnormal observations), which leads to generated reports of similar quality from both models. Second, in some reports with multiple abnormal observations, both models made mistakes on a subset of these observations, making it difficult to decide which model output was better. The integrations of fact ENT and fact ENTNLI showed improvements in the clinical metrics. We further examined whether these rewards can be Metric  BLEU4 0 . used to estimate the performance of the clinical metrics to see whether the proposed rewards can be used in an evaluation where a strong clinical information extraction system like CheXbert is not available. Table 4 shows Spearman correlations calculated on the generated reports of NLL+BS. fact ENTNLI shows the strongest correlation with the clinical accuracy which aligns with the optimization where the best accuracy is obtained with NLL+ BS+ fact ENTNLI . This correlation value is slightly lower than a Spearman correlation which Maynez et al. (2020) observed with NLI for the factual data ( 0 . 264 ). The result suggests the effectiveness of using the factual rewards to estimate the factual completeness and consistency of radiology reports, although the correlations are still limited, with some room for improvement. The evaluation with the clinically findings metrics showed improved generation performance by integrating BERTScore, fact ENT , and fact ENTNLI . As a qualitative analysis, we examined some of the generated reports to see the improvements. Example 1 in Figure 3 shows the improved factual completeness and consistency with BERTScore. The atelectasis is correctly generated and left plural effusion is correctly suppressed with NLL+BS. Example 2 in Figure 4 shows the improved factual completeness with fact ENTNLI . The edema is correctly generated and atelectasis is correctly suppressed with NLL+BS+ fc EN . These examples reveal the strength of integrating the three metrics to generate factually complete and consistent reports. Despite observing large improvements with our model in the clinical finding metrics evaluation, the model is still not complete and some typical factual errors can be found in their generated reports. For example, Example 3 in Figure 4 includes a comparison of an observation against a previous study as Images Reference R2Gen M 2 Trans w/ NLL+BS E x a m p l e 1 Largerightpleuraleffusionisunchangedinsize.Thereisassociatedrightbasilaratelectasis/scarring,alsostable.Healedrightribfracturesarenoted.Ontheleft,thereispersistentapicalpleuralthickeningandapicalscarring.Linearopacitiesprojectingoverthelowerlobearealsocompatiblewithscarring,un-changed.Thereisnoleftpleuraleffu-sion.Thereisnopneumothorax.Hilarandcardiomediastinalcontoursaredif-ficulttoassess,butappearunchanged.Vascularstentisseenintheleftaxil-lary/subclavianregion. . . . appear more prominent since . . .  in the reference but our model (or any previous models) can not capture this kind of comparison since the model is not designed to take account the past reports of a patient as input. Additionally, in this example, edema is mentioned with uncertainty as cannot be excluded in the reference but the generated report with fact ENTNLI simply indicates it as There is mild pulmonary edema. We proposed two new simple rewards and combined them with a semantic equivalence metric to improve image-to-text radiology report generation systems. The two new rewards make use of radiology domain entities extracted with a named entity recognizer and a weakly-supervised NLI to capture the factual completeness and consistency of the generated reports. We further presented a Transformer-based report generation system that directly optimizes these rewards with self-critical reinforcement learning. On two open datasets, we showed that our system generates reports that are more factually complete and consistent than the baselines and leads to reports with substantially higher scores in clinical metrics. The integration of entities and NLI to improve the factual completeness and consistency of generation is not restricted to the domain of radiology reports, and we predict that a similar approach might similarly improve other data-to-text tasks. We would like to thank the anonymous reviewers and the members of the Stanford NLP Group for their very helpful comments that substantially improved this paper.
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