Sticking to the Facts: Confident Decoding for Faithful Data-to-Text Generation
Authors: Ran Tian
Shashi Narayan
Thibault Sellam
Ankur P. Parikh
Venue:  (2020)
Keywords: Natural Language Processing
Text Generation
Data-to-Text Generation
Hallucination
Calibration
Variational Bayes

Abstract: Neural conditional text generation systems have achieved significant progress in recent years, showing the ability to produce highly fluent text. However, the inherent lack of controllability in these systems allows them to hallucinate factually incorrect phrases that are unfaithful to the source, making them often unsuitable for many real world systems that require high degrees of precision. In this work, we propose a novel confidence oriented decoder that assigns a confidence score to each target position. This score is learned in training using a variational Bayes objective, and can be leveraged at inference time using a calibration technique to promote more faithful generation. Experiments on a structured data-to-text dataset -- WikiBio -- show that our approach is more faithful to the source than existing state-of-the-art approaches, according to both automatic metrics and human evaluation.

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STICKING TO THE FACTS: CONFIDENT DECODING FOR FAITHFUL DATA-TO-TEXT GENERATION
Anonymous authors Paper under double-blind review
ABSTRACT
Neural conditional text generation systems have achieved significant progress in recent years, showing the ability to produce highly fluent text. However, the inherent lack of controllability in these systems allows them to hallucinate factually incorrect phrases that are unfaithful to the source, making them often unsuitable for many real world systems that require high degrees of precision. In this work, we propose novel confidence oriented decoder that assigns a confidence score to each target position. This score is learned in training using variational Bayes objective, and can be leveraged at inference time using a calibration technique to promote more faithful generation. Experiments WikiBio (Lebret et 2016)) show that our approach is more faithful to the source than existing state-of-the-ee approaches, according to both automatic metrics and human evaluation.
structured bata-to-tex: dataset-
INTRODUCTION
Conditional text generation is the task of generating some larget text y conditioned on source content x: It is the essence of many natural language processing problems, such as text summarization (Mani 1999), where is long document and y is more concise version, machine translation (Koehn 2009)
where x and represent equivalent text in different languages, and data-to-text generation (Kukich
1983 McKeown 1992), where æ is a structured table and y is a textual description.
While traditionally done with template-based approaches (Becker 2002 Foster & White 2004 Gatt
Reiter 20099] Reiter et al., 2005), recently neural encoder-decoder approaches (Sutskever et al. 2014 et al. [2144] Bahdanau al., 2014) have become popular approach. In this formulation, the source content is encoded with a neural architecture, and the decoder autoregressively produces a token each output position based on its internal state and the source representation. By leveraging continuous representations with rich non- loneaiira,,, encoder-decoder approaches can generate highly fluent text (Rush al. 2015 Radford al. 2019) without the need for cumbersome handcrafted rules and templates.
However, encoder-decoder architectures are inherently difficult to control, and have been shown be prone to hallucination, i.c., generating text that is fluent but infaithful to the source (Vinyals & Le 2015 Kochn & Knowles 2017 Wiseman al.) 2017 Lee al. 2018). This severe shorteoming can often limit the use of neural approaches in many real world systems, where it is not acceptable to produce output that even occasionally unfaithful.
In this work, we focus on data-to-text generation since the structured form of source content makes it relatively easy to evaluate faithfulness using both human evaluation and domain domain-specific automatic metrics (Dhingra et al. 2019). In particular, we focus on the WikiBio (Lebret et al. 2016) dataset, where the task generate sentence summarizing a tabular biography of person. Figure shows an example.
First note that the reference contains information such as bonanno crime family and informant that are true, but cannot be inferred from the source. This source-reference divergence exists in many large-scale generation datasets (Wiseman et al.] 2017 Dhingra et al., 2019). Secondly, most generation systems agnostic to this divergence and trained to maximize the log likeliood of reference. This can often encourage the models to output phrases that are unsupported by the source. For example, Figure the output of state-of-the-art generation baseline, the Pointer-Generatt

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Source (Wikipedia infobox):
Target:
Frank Lino
Reference: Frank "Carly "Lino (born October 30, 1938 Brooklyn) is Siellan-American Caporegime the Hoeanno crime
crime family who later became informant.
FBI surveillance photo
Baseline: Frink Lino (been October 30, 1938 Brooklyn New York, United Stntes) an American grmmiaa defense attorney.
Birth date Cotober 30, 1938
Birth place Gravasend, Brooklyn,
Our model: Frank Lino (bom October 30, 1938 in Brooklyn, New York, United States) is
United States
Figure Example in the WikiBio dataset (Lebret al. 2016 showing the biography of Frank Lino. The baseline Pointer-Geemerator (See ct al. 2017) exhibits hal lucinatiio..
network (See et al., 2017), which contains the phrase criminal defense attorney that is false (but loosely related to FBI in the table). Thus, hallucination can often result from the coupling of model shortcomings (e.g. lack of formal reasoning. learning false correlations), and noise/artifacts in the training data.
In this work, we propose a confidence oriented approach which assigns a learned confidence score to each decoder position, and then uses the score in two ways to reduce hallucination: (1) In test, it uses confidence to adjust the output probabilities by a calibration technique (Braverman et al. 2019). (2) In training, allowing the model to skip tokens with low confidence score to avoid training on reference phrases that are difficult to infer from the source. In Figure our approach leads to faithful generation that omits the occupation.
employ variational Bayes objective to jointly learn the confidence score while
Empirically, when evaluated on the WikiBio dataset (Lebret al. 2016), we show that our approach is considerably more faithful to the source than existing ttate-of-the-rt solutions, according to both PARENT precision (Dhingra al. 2019) and human evaluation.
RELATED WORK
Improving the fidelity and accuracy of text generation systems is an important research topic that has spawned variety of different approaches. Some focus blending extractive and abstractive methods, e.g., allowing the model to copy tokens directly from the source (Gu et al. 2016 See et al.
2017), separating content selection from generation (Zhou et al.) 2017 Gehrmann al. utilizing topic information from the source to make informed generation (Narayan et al.
2018) and
2018).
Other approaches have proposed generating more accurate using semiparametric approaches (Guu et al. 2018; Pandey et al. 2018 Peng et al. 2019), reinforcement learning-bast baased rewards (Paulus
2018 Pasunuru & Bansal 2018), semi-Man Mark-M models learn neural templates (Wiseman al.
2018)
data augmentation (Ma et al.) 2019: Kedzie & McKcown 2019), content planning (Puduppully
al. 2019a), and constrained vocabulary decoding (Wu et al. (217). While many leverage the structure of the source (Liu et al. 2018) Marcheggiani & Perez-Beltrachin 2018) or task- insights (Puduppully al. (D)9), our approach is complementary in that it uses general machine learning techniques to build confidence oriented decoder, that is more faithful to the source and robust to divergence/noise in the training data. Furthermore, many previous works rely on automatic metrics such as BLEU, which can be poorly correlated with human judgment of faithfulness (Wiseman et al. 2017: Dhingra et al.) 2019). In contrast, we evaluate on PARENT precision (Dhingra et al. 2019) metric specifically designed to capture faithfulness in data-to-text generation, and conduct rigorous human evaluation to assess hallucination in our models.
3 PRELIMINARIES
We first review the existing encoder-decoder framework (Sutskever et al., 2014], Bahdanau et al. 2014) which this work is based on. Let æ x 21 ... be the source input length and V132 ... yr be the target sequence of length T. Each token F4, y takes one value from vocabulary V. Our goal is model the conditional distribution P(y|æ) II ())
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where y-t y1 ........ee --- is the prefix of y up to the (t - 1)th token. The source be encoded by any neural network function enc, such as a convolutional neural network (CNN, LeCun et al. [990). long-short-term memory (LSTM, Hochreiter & SSchmidhuber 1997), Transformer Vasswan et al. 2017]. Let 81, 85 ene(#1,- Tg).
Define Cr IR as the d dimensional embedding of token x. Then, the probability of each target token is computed as:
P ((v yet,, x:) = exp (v, (u,Te, ey.
(1)
Σ Evev(exp Ey)
where the context vector o is given by:
a. + h4 Σ 01 + hec
(2)
In this work, we use a Luong-style attention (Luong et al. 2013) for the first term on the right hand
side of Eq. 2 defined in Eq. and the second term given by RNN state at position t, as defined in Eq. (let [-] denote concatenation):
ag.t = exp(s, Wh:)
(3)
expxp(s
exp(s Wh,)
h: = RNN(h, (e
(4)
In case the encoder-decoder is equipped with copy mechanism, the generation probability mixed with probability of copying from the source (Gu et al. 2016 See et al. 2017):
P(yeleee 2) = 85n ( yet, x) + (1 Pt
BI,t a:
Σ
(5)
where pt is the probability of doing generation instead of copying at step t, and B,, is an attention weight that the copy mechanism paying to position the source. The sum is taken over all positions where the word is the same as y/-
4 MODEL
Our approach is based on confidence score at each decoding position, and the idea is originated from an observation on the hallucinations shown in Figure hallucination often occurs when the table lacks some field that usually exists (e.g. Occupation), and the baseline model is trying to make up (more examples are found Appendix). In such cases, the generation probabilities for the hallucinated tokens can still be high (e.g. > 0.5), so it is difficult to tell hallucination from faithful generation. However, if some usual field is missing in the table, one might tell the difference by assessing the attention in the encoder-decoder. Our preliminary investigation supported this idea: defined an attention score A, using the length of the attention vector relative to the hidden state, as the following (let |-||| denote the Euclidean norm):
A,
6
hell
and compared the scores between hallucinated and faithful generations. We found that A, drops in the hallucinated cases. Then, refined our attention score with the copy mechanism:
: p.c0 A, (1 p0.
As illustrated in Figure 2. A, is usually high (~ 0.9) when the model is copying from the source (c.g. Campbell), medium (~ 0.4) when it is generating based on some source informa- tion (e.g. <unk>>, and low (~-0.2) when generating function words, template elements, or when the corresponding field is missing (more examples are found in Appendix).
While it is possible our approach could extend to other types of decoders, our current formulation of the confidence score specifically uses RNN with attention.
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Christian Campbell
Aftertinn Siarr
Pare- M
Confidenoe Sizzen
Bith
Chrittnn Campbel
Bith date
1979
0.25
Occupation
christian campbell
born
1979
)
is
<unk>
paet
Figure 2: Examplc of learned attention score, base-LM probability, and confidence score. For content words the base-LM probability is lower, and the confidence score depends more on the attention score.
Thus, the attention from function words and template elements. Therefore, we further introduce an unconditioned language model called base-LM. The base-LM does not have access the source data, if it can predict a token as precisely as the encoder-decoder, that token is probably an element of a general linguistic construction that does convey source information. Then, define confidence score as an interpolation between the encoder-decodee P x) and the base-LM ((yo))<:):
recognize faithful content words, but it cannot distinguish hallucination
(8) For function words and template elements, we expect both P(y (/ x) and P(yll g<t)) to be high, so the confidence score Cyv (tt .) will be high no matter what attention score is. the other hand, we expect Pu a) to be higher than P( y<t) for content words, and the confidence score will largely depend on the attention score in this case.
C(v+ Met. a:) == Å, P(y yet, a)) (1 P.) y<t))
From a high level, our confidence score keeps a balance between two factors:
How much the model should rely on the source for predicting this position, is reflected by combining the encoder-decoder with the base-LM.
How much the model actually relies on the source for this position, measured by the attention score.
Intuitively, it is reasonable for system depend mostly language modeling predict function words that make the generation fluent, but it should consult more the source data to predict content words. For example, given partial generation "Christian Campbell is _", one could predict that the next token is mostly likely "a", "an" or "the", based on language modeling. However, if model predicts "American" as the next token to "Christian Campbell an should be based on a field such as "Nat i Looooniittt maaaity: U.S. in the source, rather than the language tendency that "American" likely to appear after the phrase "is an". A typical neural network can make predictions based on both reasons with little controllability; this, we contend, is general cause of hallucination. By introducing the confidence score and enforcing "confident prior" on our model, we can gain some control on these two.
In order to further strengthen control, we tweak the input-feeding of our RNN models as below:
g: RNN (g: RNN( (g- P
h: RNN(h+-1, (NNNhh-..ley..1- (G(a--))
(10)
Here, 9: the hidden-state of the base-LM,, and the input embedding 6 in Eq. is weighted by component of the previous confidence score. We found this weighting scheme to decrease dependence of the base-LM on content words, seemingly resulting in a model of soft templates, as
shown Figure (see Appendix for more examples). We also added stop-gradients (SG) to prevent information at the current step from being propagated to previous steps, and used Eq. 10 instead of Eq. They seem to result in more reasonable attention scores.
The confidence score is leveraged in two ways, as we describe in the following:
At test time, augment the generation probability with the confidence score, using calibration technique (Section 4.I). It allows us to weigh more on the confidence of generation, without sacrificing perplexity (Braverman et al. 2019)
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X
Salome Jens
Confidence Soore:
1992
Birth data
1832
y2: vakone
Jrn
bcm
1935
ddage
fin
avd
telel of 97786
Birth place
Vilaasion,
keop
Ieep
keep
teep
xtsp
atop
Waaomit
Q
Rendom SundeomSubbeerng Subbang
Occupation
Actroos
1985
P(z (c)
Figure Example of sampling sub-sequence according the confidence score. Our variational Bayes
objective combines the sampling probability Q(= a:) and the generation probability P(=|æ).
In training, we allow the model to skip some tokens with low confidence score in order avoid training noisy references scarce examples (Section 4.2). However, since the confidence score itself needs to be learned during training, we use variational Bayes objective to formalize this symbiotic cycle (Kingma & Welling 2014).
4.1 CALIBRATION
In order to use the confidence score to promote faithful generation, apply calibration tech- nique (Braverman al. 2019) which augments the generation probability as follows:
("(llWee = (G(C()llee.:)))) (( Ewev (C)
(11)
Here, P" Bee. x) is one paramereet family probability distributions called the calibration of P(ullyee, æ) to C3v Vee, x). Note that SG stops gradients so that only the parameter is updated
during training. In order to learn minimize the negative log- legliiklliood of ps (ye (elyyee, x) jointly with P( yet, x) and Ply y<t):
Cjoiml (y. x))
log P* (yelMee x) log P(y yer, a:) log P(yel
(12)
Since P(yelBer x) a special case of (M Net. x) (namely at λ = 0), the training perplexity of p. (yr 9/ce, æ) is at most P(y (),,,)), v). In practice, is initialized as and found converging to positive value (Section.) 5.4). Therefore, the calibration trick improve confidence of generation without sacrificing perplexity.
4.2 TRAINING WITH A VARIATIONAL BAYES OBJECTIVE
In practice, the training data of a conditional text generation task almost always contain noises and/or scarce examples. In order to reduce the impact of such outliers and train confident generation model, we allow the model skip some tokens in the training data when feels unconfident. For this purpose, use the confidence score sample sub-sequence of each training target, and minimize the negative log-liketn logeliiel on that confident sub-sequence. However, since the confidence score itself needs to be trained, we use variational Bayes objective to formalize the problem.
Specifically, for each target = /1/2... yr, we define z = 21 2n = (.(1) ... V.(R) as a latent sub-sequence of y, which consists of confident tokens of length Rt. Here, :: |/|] + [T'] is an
inclusion of indices. We assume that z is generated by the calibrated probability pa (2, x)):
R
P(z x
II
Zer, x).
(13)
Then, we connect P(z|<) to the probability of training target P(y|z). From Bayes rule:
P(y œ) = P(y|z, 2. P(x(x)
(14)
P(z x)
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We assume that P(y/|z,2) x) 1 for every training example because the training data is uniquely given. Then, we regard x as a sequential "keep/skip" labeling over y. and define a probability distribution
[c(kee)) & ())))))))))))))) Q. =
Where keep means yr is in x Where skip means is not in
(15)
Qi(kkip) x x
to sample a sub-sequence according to the confidence score (Figure 3). Here, p and are hyper- parameters. Now let
Q(= y.
Qr.
(16)
and the idea is to use Qz æ:) as an approximation the unknown posterior P(zly, x)) Eq. By taking - log(-) of both sides in Eq. 14 and trivially introducing log ((=ly.xx)) we get
- log P 2) = log Q( + log Q( v) - log P(z æ).
(17)
P(zly,a) x)
Then, we take the expectation =)(= of both sides and note that (x( loog [[
( x)] 2 O, so
log P(y x)) I EQ(=)a))) log Q(2 y. yy - log P( m²)]
(18)
The variational Bayes objective is minimize the upper bound the right hand side of Eq. T8 In practice, it is computationally expensive to explicitly calculate E by enumerating all sub-sequences of y. because the number of sub-sequences exponential the length T. Thus, apply a Monte Carlo method which calculates (Q(=//.))) by sampling from Q æ). In order to back-propagate gradients through the expectation (Q(= [+] as well, the loss function is given as follows (Paisley et al. 2012):
1
L ( v)) K
log Q(2k yy - log P(zk |
+ SSG (og Q y. log P(z* x)) log Q(zu y. y. (19)
Here, is the number of samples taken, and is hhper- controlling how fast the gradients go through (,))) [.). However, since define [(z x>) in Eq. 13 by the calibrated probability, which only learns one parameter K, we add joint learning terms into Eq 19 to make the final objective:
K
1
C(y, =
log Q(z x) + (=k,
K
=== y.m)
+ A AG (zxx yy 1 log ((zx x) log Q(z y. x.
(20)
In order to sample sub-sequences from Q(2ly, æ)), apply the same Gumbel-max trick in Kool et al. (2019). Although the random sampling is purely based on learned probability distribution, without any constraints make it fluent, surprisingly the model still learns to generate fluent text.
EXPERIMENTS
Although our approach could apply to many conditional text generation tasks, in this work we consider data-to-text generation, in which the source is structured data and the target is natural language text describing the data. Usually, the data have concise semantics and simple structure, which makes it easy check the facticity of the generation.

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Automatic Metries
Human evaluation
Model
BLEU
PARENT (Prec. / Rec. /FI) Avg Len.
Fnithful &
Avg Cov.
Fluency %
BERT Rothe
44.83
77.62
53.13
77.6
98.5 /9.4
Structure-Aware Seg2Son Lau et
2018
45.36
73.98
44,02
52.81
23.1
66.1
4,47
88.6/
Pointer Generator (See el 2017
41.07
77.59
42.12
52.10
19.1
80.3
4.24
93.1 96.0
Confident BERT- BoRR--i--NN (This Work)
33,30
77.98
37.21
/ 47.90
16.6
85,20
3,90
92.3 19.1
Confident Pointer-Generator (This Work)
38.10
79.52
40.60
51.38
17.0
86.8
4.05
95.4/96.3
+1treabold=0.125
36.62
80.15
39.59
/ 50.50
16.4
90.7*
4.01
91.6/92. 92.2
Table I: Performance on WikiBio test set. Two Fluency measures differ in whether to include sentences graded Mostly Fluent. Starred numbers are statistically significant against baselines (p < .001), by bootstrap test.
5.1 DATASET AND EVALUATION METRICS
The WikiBio dataset (Lebret et al. 2016) contains 728.321 biographies paired with infoboxes, taken from the Sep-2015 dump of English Wikipedia, and splitted into train/valid/tes sets in a 8 ratio. The biography text is the first sentence of the Wikipedia page (26.1 words on average). Infoboxes have 12.1 non-empty fields on average.
For automatic metrics, we report BLEU (Papineni et al. 2002). J as well as PARENT (Dhingra et al., 2019), a metric designed to mitigate the shortcomings of BLEU on structured data-to-text generation.
For human evaluation, we obtain crowd-source annotations on examples randomly chosen from predictions on the WikiBio test set, the same 1000 for each model. Examples from different models are mixed and randomly shuffled, with model names hidden from the raters. We instruct the raters to grade on each of criteria: faithfulness, coverage, and fluency, below:
Faithfulness (precision). We define a sentence to be faithful if all the information in the proposed sentence is supported by the table or the reference. A single hallucinated piece of information makes the sentence non faithfu..
Coverage (recall) The number of table cells that contain information present in the sentence.
Fluency- A sentence is defined to be fluent if it clear, natural, and grammatically correct. Raters choose among three options: Fluent, Mostly Fluent, Not Fluent.
An ideal system would always produce fluent and faithful text with high coverage. The complete annotation instruction is given in the Appendix. We assigned 5 raters who are well-trained on this task, and conducted 5-way annotation on a pilot batch of 100 examples. The unanimous agreement rate is 86% for faithfulness and 99% for fluency, with 74% of the examples judged as faithful and 98% as fluent.
5.2 EXPERIMENT SETTING
We compare the following systems:
BERT-to-BERT (Rothe et al. 2019): A Transformer encoder-decoder model (Vaswani et al.
2017 where the encoder and decoder are both initialized with BERT (Devlin et al.) 2018).
Structure Aware Seq2Seq (Liu et al. 2018): A state-of-the-art method on the WikiBio
dataset in terms of BLEU.
Pointer- Generator (See et al. 2017): A Seq2Seq with attention and copy mechanism (our
implementation).
Confident BERT-t--NN (This Work): A Transformer encoder initialized with BERT checkpoint, and a GRU (Cho et al. 2014) decoder with our confident decoding method.
Confident PeinterrGenerator (This Work): Pointer-Generator model with confident decoding.
We built our systems using Tensorflow (Abadi al. 2016). Infoboxes are linearized into sequences, with field names and values separated by special tokens. For BERT-to- -ERT and Confident BERT- to-RNN, pre-trained a BERT checkpoint the Books corpus (Zhu et al., 2015) only, since the original BERT was trained on Wikipedia that overlaps with the test targets in WikiBio. Other hyper-parametee settings are given in Appendix.
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BLEU
PARENT (Prec./ / Rec./FI)
Avg Len.
Confident Peinter-Generator (This Work)
38.10
79.52
40.60
51.38
17.0
LM
39.39
78.77
41.55
52.08
No calibration
37.89
79.47
40.47
51.26
16.9
No variational
41.29
78.25
42.40
52.552
18.9
Pointer-Gencrator Truncated
41.07
77.59
/
42.12
/ 52.
19.1
35.50
77.68
38.16
48.66
Table Ablative three components of our confident decoding method, and a truncation test.
5.3
MAIN RESULTS
Table shows the results. According to human evaluation, our approach gives a clear improvement in faithfulness over the baselines, with some drop in coverage. To further measure the validity of our confidence score, we postprocessed the output to remove words with lower confidence than 0.125. This thresholding technique gives further gains to faithfulness, while sacrificing some fluency.
Among the automatic metrics, PARENT precision and recall seem correlated to faithfulness and coverage respectively, and our approach achieves the highest precision score. BLEU, perhaps because of its length penalty that rewards longer generations, seems more correlated to coverage rather than faithfulness. Regarding the baselines, Pointer Generator is the most faithful, suggesting that pretraining might help fluency while the copy mechanism can be valuable for faithfulness.
that BERT-to-BERT is the most fluent while
5.4 ABLATIONS
Our confident decoding method has three novel components: (1) The use of base-LM to define confidence score; (2) The calibration technique to adjust output probability; and (3) The variational Bayes objective to train confident model. In this section, assess the effects of each component by an ablative study. We start from the Confident Pointer-Generator, and in each test replace one component by trivial alternative: (1) In order assess the effects of using the base-LM in the confidence score, we instead use P( V<c, x)) directly as confidence, and train models with the same syper-parameter search. The results on WikiBio are shown Table - as "No base-LM" (2) We use P3 (3 æ) instead of PM (y: B<r. 2) at test time ('No calibration"). to assess the effects of calibration. The model is the same as Confident (3) Instead of the variational Bayes objective, we use the joint training loss Lyxim in Eq. 12 without sampling sub-sequences from training targets (No variational).
Sinter-Generator The learned к was 0.035.
As we can see from Table all three components improve PARENT precision. While the improve- ment by calibration is the smallest, the technique also improves PARENT recall and BLEU score at the same time, making an easy choice. The other techniques trade recall for precision, making them useful for tasks that require high degree of faithfulness. When all three components disabled, the model exactly the same as our implementation of the Gointer-Generator. Every component Genterto with "No
improves PARENT precision upon it well. Especially, comparing variational" shows again that joint training with calibration improves all metrics.
We also note that the average lengths of generations by confident decoding models shorter. While there exists heuristics such as length penalty (Wu et al., (0166) to encourage longer generation inference time, shorter generation is not trivial. In the "Truncated" setting, we truncate predictions by the Pointer-Generator two words each to match the average length of Confident Pointer-Generatee The PARENT precision by our confident decoding method is not trivially achieved by truncation.
CONCLUSION
In this work, we have proposed a confidence oriented decoder that achieved more faithful generation on the WikiBio dataset than existing state-of-the the-hhhrrrtt approaches. Our method is general in principle, so it could potentially be combined with other approaches and/or adapted to other forms of conditional text generation such document summarization and machine translation.
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7 APPENDIX
7.1 SAPER-PARAMETERS
Model
Warmup Steps
Learning Rate
RNN Dropout
RNN Dim.
Beam Size
Conlident BERT to RNN
40000
0.05
0.1
768
8
0.75
1/8
1/64
Pointer- Generntor
Table hyper-paramette
hyper-paramann search for p. y within the range [0.25, 0.5, 0.75, 1]. [1/16, 1/4 and [1/64, 1/16, 1/4, 1], respectively. Model selection is based on PARENT F1.
For Pointer-Generator and Confident Obinter-Generatorr we use GloVe (Pennington et al. 2014) as the input word embedding and truncate the vocabulary size 5,000; the two models share the same hyper-parameter settings. For our confident decoding models, there are additional hyper-parameters P.? defined in Eq. [5] and K, À defined in Eq. 19 The hyper-parameters shown Table The optimizer is Adam Kingma & Ba 2015).
7.2 HUMAN EVALUATION INSTRUCTIONS
Here are the detailed instructions for our human evaluation template:
A writer has the following background knowledge and is given table:
Reference sentence shown as background knowledge>
Table>
The writer read the table and produced the following sentence:
< Sentence generated by model>
We wish to evaluate the quality of the sentence.
1. How fluent is the sentence?
Fluent: It is clear, natural, and the grammar is correct. It reads like if it was found in book.
Mostly Fluent: It has a few errors or it does not sound natural, but you understand it. Not Fluent: It has many errors and/or you can hardly understand it.
Examples:
Sentence
Decision
alfred angas scott 1875 1923 was motor- cycle designer born in manningham bradford, who lived in
Not fluent: the sentence stops abruptly. It is unnatural, it does not make any sense.
alfred angas scott 1875 1923 was motor- cycle designer designer born in manningham bradford.
Mostly fluent: the repetition "designer de- signer" is not natural, but the sentence makes sense.
alfred angas scout 1875 1923) was mo- torcycle designer, bradford, lived in england u.k., was british by nationality, and was buried at the undercliffe cemetery in bradford.
Mostly fluent: there are no mistakes, but the sentence contains too much information. This is unnatural.
born in manningham
2. Please compare carefully the content of the sentence to the content of the table. How many cells from the table did the writer use to produce the sentence? (Click on the cells in the table above to update the counter)
A sentence is faithful if it contains only information supported by the table or the writer's background knowledge. It should not add any additional information, even if the information is true or interesting. Please compare once again the content of the sentence to the content of the table and background knowledge. How faithful is the sentence?
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every part of the sentence is supported by the table andlor background knowledge. Mostly Faithful: every part of the sentence can be linked to some evidence in the table or the background knowledge, but is not fully supported. This should only be used for rare edge cases.
Faithful:
Not Faithful: The sentence contains information that is not supported by the table or background knowledge.
The examples are based the following background knowledge and table:
alfred angas scott 1875 1923 was a british motorcycle designer.
birth date
birth place
death date
nationality
residence england, uk.
occupation
1875
manningham, bradford, united
1923
british
motorcycle designer
and manufacturer
kingdom
Examples:
Sentence
Decision
alfred angas scott 1875 1923 was a british motorcycle designer and founder of the scott motorcycle company.
Coverage:
Not faithful: the table does not mention that A. A. Scott was the founder of the Scott Com-
pany.
alfred angas scott 1875 1923 euro- pean molorcycle designer.
Coverage: 4
Faithful: All the information is supported be- cause England is located in Europe.
We have also discussed with the lead annotators about many other examples. We have made sure that: (a) Valid inferences (e.g. inferring nationality from birth place) considered faithful; (b) If a piece of information exists or can be inferred from the table, the corresponding cell should be highlighted, even if the information was also in the background knowledge; (c) Only cell should be highlighted for one piece of information.
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Table
Pointer Generator
Confident Pointer Generator
Team missing
rohan robertson born 21 august 1961 )
rohan robertson born 21 august 1961 is former australian rules footballer who played the victo- rian football league.
is former australian rules footballer who played with carlton the victorian foot- ball league.
name missing
Middle
walter herbert smallwood april 24, 1893 april 29, 1967 was pitcher in major league baseball.
walter smallwood ( april 24, 1893 -april 29, 1967 was pitcher in major league baseball.
Residence miss- ing
nellie wong born september 12, 1934 in oakland california poet activist, feminist, and feminist activist who lives and works in los angeles califormia united states, where she
nellie wong born september 12, 1934 is american poet and tivist.
american
Name missing
gene ( born december 21 1941 is former american football tight end in the national football league.
( (born december 21 1941 ) is for- mer american football tight end the national football league for the minnesota vikings
Nationality
constant vanden stock june 13, 1914 april 19, 2008 was an american figure skater
constant vanden stock ( (uune 13, 1914 april 19, 2008 was a.
& occupation missing
Table fields are: name, national- ity, years and teams
richard lloyd lloyd is british racing driver who won the gti engincering cham- pionship in 1982 driving with gti engi- neering. richard lloyd racing gti engi- neering and lloyd lloyd racing at the age of 14.
richard lloyd is former racing driver
District: 3rd
robert i. marshall born october 16, 1946 in wilmington, delaware an american politician and democratic member of the
robert i. marshall ( (born october 16 1946 in wilmington delaware is an american politician and demo- cratic member of the delaware sen- ate since january 9, 1979.
delaware senate since january 9, 1979 representing district 41.
Known for: En- glish and Welsh dictionary
thomas edwards 1779 4 june 1858 was an english author <unk>, and <unk>, who spent most of his life in the english and english literature of the en- glish and english dictionary literature.
thomas edwards ( 1779 4 june 1858 was welsh author.
Table 4: Generation examples. Red tokens are hallucinated.
7.3 HALLUCINATION EXAMPLES
In Table 4 show some typical case where the Pointer Generator baseline hallucinates but the Confident Pointer Generator does not. The examples are taken from the WikiBio validation set.
In the first five examples (i.e. "Rohan Robertson". "Walter Smallwood", "Nellie Wong", "right end" and "Constant Vanden Stock"), ) some information is missing in the table while the baseline made up. Our Confident Pointer Generator model learns to omit the missing fields, although by doing this, two the generated sentences are no longer fluent (i.c. "tight end" and "Constant Vanden Stock").
In the next example (i.e. "Richard Lloyd"), the baseline seems have learned weird language modeling from some similar training points, and tries to generate more than the table contents; our Confident Pointer Generator is more conservative.
In the last two examples (i.e. "Robert Marshall" and "Thomas Edwards"), there are corresponding fields in the table but the baseline didn't learn to generate the text correctly, possibly because these
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Frank Lino
sevotiac pluto
Octater Totk
Oraworrd Boadlers
New Youk teide
Aaamico Seare
Conteerce SOorS
Cornelia Molnar
0.76
Fukuraa
21 Dicoente 1941
Chooge diaie
Alrilaat Sour
DiaeL
Due betce Scae
Tiphaadd
075
Callago
025
Dalad
Debxt
Uinearta 1irgge
Powtlear
Androw Hunt ( painter)
Bethoome
Zevil The belry
Alertin
DemKLN
Conteuree
2.5
Natemalty
34thh
1790
1801
Koawe
Delpy
Devid
ARSIDOn DOPe
BAXCAM
COVDEENC Sooe
Warkyines
Koowe
CDO D'onns
925
Pidde
then
1h:mm 51/7
plyice
Figure Examples of the learned attention score, base-LM probability and confidence score.
fields should not be simply copied but there are not enough training data to learn the correct generation. Our Confident Pointer Generator model simply omits these fields.
7.4 ATTENTION SCORE, BASE-LM PROBABILITY AND CONFIDENCE
In Figure 4 show the attention score, base-LM probability and confidence score learned by Confident Geintar-Generator model. The examples are taken from WikiBio validation set.
The first three examples (i.e. "Frank Lino", "Cornelia Molnar" and "tight end") reference sentences, and our model successfully detected the tokens not supported by the table (i.e. Occupation missing in the Frank Lino table, the Cornelia Molnar table only has name, and the tight end table does not have name), which suggests that our model may correctly skip unsupported tokens during training.
The last two examples (i.e. "Andrew Hunt" and "David Delpy") are model generated sentences, and we can see that the attention score intuitively measures how related a token the source table, and the base-LM successfully learned soft templates.
16


Reviews:
[{"review_id": "rJgi9SljqS", "reviewer": "AnonReviewer3", "summary": "", "questions": "", "limitations": "", "rating": 8, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "This paper presents a method for conditional text generation that has higher factual precision, minimizing hallucination of facts. The method involves predicting confidence of generation at each time step and using this confidence measure to skip tokens during generation and calibrate output probabilities in test time. Their method achieves SoTA performance on automatically measured precision and human evaluated \"faithfulness.\" However their method does see a drop in recall (automatic metric and human evaluation).\n\nComments and issues,\n- The intuitive explanation for the confidence score is a little confusing. In Section 4, page 3, you say that \"If a token is likely a content word (i.e. when its generation probability by the encoder-decoder is much higher than the unconditioned language model), but the attention score is low, then the token might not be predicted based on the source, and could be hallucination.\" However, this doesn't seem like an airtight conclusion. Isn't it possible that the base-LM and enc-dec model have similar probabilities for a content word with the enc-dec attention being low? This seems possible given your observation that low attention to the source is what may be causing content hallucination. This same thing is essentially restated in section 4.1 \"we expect P(y_t |y_<t, x) to be higher than P(y_t | y_<t) for content words so the confidence score will largely depend on the attention score\", which seems more tangled up since P(y_t |y_<t, x) inherently depends on the attention score. This is all clarified when you explain the alteration made to the base-LM. I would recommend rewording/rearranging some of the earlier explanation for the efficacy of the confidence score since it seems that the alteration to the base-LM is an essential part of the explanation. \n- Need some explanation for Equation 6. I don't really get the intuition behind it.\n- The presented results are pretty good! However, I would like to see some numbers on average score across a few runs.\n- It would also be good to see results on one more dataset like E2E.\n- Provide a little more detail on human evaluation, you don't even mention if the evaluation was done with crowd-workers or another pool of people like grad students. How many annotators? What is the inter-annotator agreement? What was the prompt/structure? Human evaluation of models is notoriously difficult, more details would give some more weight to the results.\n\nI think this is a well written paper with thought out experiments. I recommend it be accepted to ICLR. I'd also be curious to see some future work that improves, or at least maintains recall, while keeping the higher precision.\n\nMinor requests/recommendations: \n- Include more examples of generations. Could be an appendix.\n", "extra_scores": {"experience_assessment": "I do not know much about this area.", "review_assessment:_checking_correctness_of_derivations_and_theory": "I assessed the sensibility of the derivations and theory.", "review_assessment:_checking_correctness_of_experiments": "I assessed the sensibility of the experiments.", "review_assessment:_thoroughness_in_paper_reading": "I read the paper at least twice and used my best judgement in assessing the paper."}, "extra_text": {}}, {"review_id": "HylX_BRscS", "reviewer": "AnonReviewer4", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "The authors propose several approaches to making a data-to-text generation system more precise, that is, less prone to hallucination.  In particular, they propose an attention score, which attempts to measure to what degree the model is relying on its attention mechanism in making a prediction. This attention score is used to weight a mixture distribution (a \"confidence score\") over the generation model's next-word distribution and the next-word distribution of an unconditional language model. The learned conditional distribution can then be calibrated to the confidence score. The authors also propose a variational-inference inspired objective, which attempts to allow the model to ignore certain tokens it isn't confident about. The authors evaluate their approach on the WikiBio dataset, and find that their approaches make their system more precise, at the cost of some coverage.\n\nThis paper is well motivated, timely, and it presents several interesting ideas. However, I think parts of the proposed approach need to be better justified. In particular:\n\n-  What justifies defining the attention score A_t in this way? First, is there an argument (empirical or otherwise) for using the magnitude of the attention vector (rather than some other statistic)? Is it obvious that if the attention vector has a high magnitude then it ought to be trusted? Note that this might be a reasonable assumption in the case of a pointer-generator style model, where a single attention vector is used both for attending and for copying, but in a model where attention isn't constrained in this way the magnitude of the attention vector may be misleading.\n\n- The variational objective seems difficult to justify. First, I don't understand what it means for p(y | z, x) to be assumed to 1. Is this for any z (in which case y is independent of z)? Otherwise, how can it be removed from the objective? (Put another way: Equation (17) is not in general true; it neglects an expected log likelihood term). I'm also not entirely clear on how Equation (12) is modeled: do the z's really only rely on the other sampled z's? Doesn't that require a different model than the one that calculates P^{\\kappa}?\n\n- Somewhat minor: the claim that optimizing the joint objective needn't hurt perplexity relies on kappa being 0; have you confirmed empirically that when it isn't zero the perplexity improves over the baseline model?\n\n- Finally, I'm not sure I understand why there needs to be a stop-gradient in equation (4). It would be nice to also verify empirically that this is important.\n\n", "extra_scores": {"experience_assessment": "I have published one or two papers in this area.", "review_assessment:_checking_correctness_of_derivations_and_theory": "I assessed the sensibility of the derivations and theory.", "review_assessment:_checking_correctness_of_experiments": "I assessed the sensibility of the experiments.", "review_assessment:_thoroughness_in_paper_reading": "I read the paper at least twice and used my best judgement in assessing the paper."}, "extra_text": {}}, {"review_id": "HklYjjdaKH", "reviewer": "AnonReviewer2", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "This paper studies the problem of data-to-text generation so that the generated text stays truthful to the data source. The idea of the paper is use a learned confidence score as to how much the the encoder-decoder is paying attention to the source. The paper includes several components, 1. unconditioned language model to incorporate the confidence score, 2. use calibration techniques to adjust the output probability; 3. variational bayes objective to learn the confidence score. \n\nThe paper has good motivations and is quite well-written. The problem is of great pragmatic interest. In the experimental part, the authors demonstrate the effectiveness of the proposed algorithm.\n\n1. For training part, regarding the language model and  variational bayes objective being trained jointly, does it have convergence problem? What is the motivation of not training them jointly?\n2. Will the code be released and the human evaluation be published?\n3. There are some importance baseline missing, such as [1], [2], [3]\n\n[1] Marcheggiani, Diego, and Laura Perez-Beltrachini. \"Deep graph convolutional encoders for structured data to text generation.\" arXiv preprint arXiv:1810.09995 (2018).\n[2] Ratish Puduppully, Li Dong, and Mirella Lapata. \"Data-to-text Generation with Entity Modeling.\" arXiv preprint arXiv:1906.03221 (2019).\n[3] Ma, Shuming, et al. \"Key Fact as Pivot: A Two-Stage Model for Low Resource Table-to-Text Generation.\" arXiv preprint arXiv:1908.03067 (2019).\n", "extra_scores": {"experience_assessment": "I have read many papers in this area.", "review_assessment:_thoroughness_in_paper_reading": "I read the paper at least twice and used my best judgement in assessing the paper.", "review_assessment:_checking_correctness_of_experiments": "I carefully checked the experiments.", "review_assessment:_checking_correctness_of_derivations_and_theory": "I assessed the sensibility of the derivations and theory."}, "extra_text": {}}, {"review_id": "BJePCEiCKH", "reviewer": "AnonReviewer1", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "This paper aims to solve the unfaithful generation problem for a specific data-to-text generation task, i.e. wikibio dataset. The wikibio dataset has a specific feature, where the output doesn't often reflect the input info box. This will cause the traditional seq2seq-style neural generation models to hallucinate frequently since the training objective is often based on word likelihood.\n\nThe paper thus design a confidence scorer that estimates whether a word should be generated according to the source information. This score is used in both training and testing. In training, it helps avoid learn to generate the low confidence words. In testing, it is used to adjust output probabilities.\n\nOverall, I think this is an interesting idea. However, the design of confidence score highly rely on the attentions calculates from the generation process, and whether attentions can be reliably estimated is questionable. Maybe it would be useful to show some statistics (not just manually picked examples) on the hallucinated words, and see what's the portion of them are due to \"flattened\" attentions.\n\nFurthermore, the experimental results are not convincing. The generations of the proposed models are significantly shorter (might be the result of training, see my comment below about 4.3), the results are mixed, both coverage and fluency are worse. Wrt results, since the dataset is from Wiki, BLEU should be pretty indicative of the generation quality. And we do see significant drop of the proposed model.\n\n\nMore comments:\n- Eq 6 needs to be better explained. I don't know if this is the common way to calculate attentions, or I misunderstood the equation. \n\n- In 4.3, I'm not sure if I can understand it correctly. When the authors say \"minimize the negative log-likelihood on the confidence sub-sequence\", does it mean words not in the subsequences are ignored? Won't this hurt the language modeling part? I.e. cause the ungrammaticality? Is this why the fluency scores are low in Table 2?\n\n- If the authors want to show their model improve faithfulness, sample outputs should be shown.", "extra_scores": {"experience_assessment": "I have published in this field for several years.", "review_assessment:_thoroughness_in_paper_reading": "I read the paper thoroughly.", "review_assessment:_checking_correctness_of_experiments": "I carefully checked the experiments.", "review_assessment:_checking_correctness_of_derivations_and_theory": "I assessed the sensibility of the derivations and theory."}, "extra_text": {}}]

Author rebuttal:

Meta-review: This paper proposes to improve the faithfulness of data-to-text generation models, through an attention-based confidence measure and a variational approach for learning the model.  There is some reviewer disagreement on this paper.  All agree that the problem is important and ideas interesting, while some reviewers feel that the methods are insufficiently justified and/or the results unconvincing.  In addition, there is not much technical novelty here from a machine learning perspective; the contribution is to a specific task.  Overall I think this paper would fit in much better in an NLP conference/journal.
Decision: Reject