RainProof: An Umbrella to Shield Text Generator from Out-Of-Distribution Data
Authors: Maxime DARRIN
Pablo Piantanida
Pierre Colombo
Venue: Submitted to ICLR 2023 (2023)
Keywords: NLP
OOD detection
natural language generation

Abstract: As more and more conversational and translation systems are deployed in production, it is essential to implement and develop effective control mechanisms to ensure their proper functioning and security. An essential component to ensure the safe behavior of the system is out-of-distribution (OOD) detection, which aims to detect whether an input sample is statistically far from the training distribution. While OOD detection is a widely covered topic in classification tasks, it has received much less attention in text generation. This paper addresses the problem of OOD detection for machine translation and dialog generation from an operational perspective. Our contribution includes (i) RAINPROOF a Relative informAItioN Projection Out OF distribution detection framework and (ii) a more operational evaluation setting for OOD detection. Surprisingly, we find that OOD detection is not necessarily aligned with task-specific measures. The OOD detector may filter out samples that are well processed by the model and keep samples that are not, leading to weaker performance. Our results show that RAINPROOF breaks this curse and achieve good results in OOD detection while increasing system performance.

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RAINPROOF: AN UMBRELLA TO SHIELD TEXT GENER- ATORS FROM OUT-OF-DISTRIBUTION DATA
Anonymous authors Paper under double-blind review
ABSTRACT
As more and more conversational and translation systems are deployed pro- duction, it is essential to implement and to develop effective control mechanisms guaranteeing their proper functioning and security. An essential component to ensure safe system behavior is nut-of-distributin (OOD) detection, which aims at detecting whether an input sample is statistically far from the training distribution. Although OOD detection is a widely covered topic in classification tasks, it has received much less attention in text generation. This paper addresses the problem of OOD detection for machine translation and dialog generation from an operational perspective. Our contributions include: (i) RAINPROOF a Relative informAltion Projection ODD detection framework; and (ii) more operational evaluation setting for OOD detection. Surprisingly, we find that OOD detection is not necessarily aligned with task-specific measures. The OOD detector may filter out samples that are well processed by the model and keep samples that are not, leading to weaker performance. Our results show that RAINPROOF breaks this curse and achieve good results in OOD detection while increasing performance.
INTRODUCTION
Significant progress have been made in Natural Language Generation (NLG) in recent years with the development of powerful generic (e.g., GPT (Radford et al., 20;8; 2019; Brown et al., 2020)) and task-specific (e.g., Grover (Zellers et al., 2019), Pegasus (Zhang et al., 2020) and DialogGPT (Zhang et al., 2019)) text generators. Text generators power machine translation systems or chat bots that are by definition exposed to the public and whose reliability is therefore a prerequisite for adoption. Text generators are trained in the context of so-called closed world (Antonucci et al., 2021; Fei & Liu, 2016), where training and test data are assumed to be drawn i.i.d. from a single distribution, known as the in-distribution. However, when deployed, these models operate in an open world (Parmar et al., 2021; Zhou, 2022) where the i.i.d. assumption often violated. This change in data distribution is detrimental and induces drop in performance illustrated in Tab. 3 and Tab. 4. Thus, to ensure the trustworthiness and adoption, it is necessary to develop tools to protect them from harmful distribution shifts. For example, trained translation model is not expected be reliable when presented with another language (e.g. a Spanish model exposed to Catalan, or a Dutch model exposed to Afrikaans) or unexpected technical language (e.g., a colloquial translation model exposed to rare technical terms from the medical field).
Most of the existing research, which aims protect models from Out-Of- -f--trr-iitiittoonnn (OOD) data, focuses on classification. Despite their importance, (conditional) text generation has received much less attention even though it is among the most exposed applications. Existing solutions fall into two categories. The first one called training-awas aware methods (Zhu et al., 2022; Vernekar et al., 2019a;b) modifies the classifier training by exposing the neural network to OOD samples during training. The second one called plug-in methods aims at distinguishing regular samples in the in distribution (IN) from OOD samples based on the behavior of the model on new input. Plug-in methods include Maximum Softmax Prediction (MSP) (Hendrycks & Gimpel, 2016) or Energy (Lee et al., 2018a) or feature-base anomaly detectors that compute a per-class anomaly score (Ming et al., 2022; Ryu et al., 2017; Huang et al., 2020; Ren et al., 2021a). Although plug-in in methods seem attractive, their adaptation to text generation may not be straightforward. The sheer number of words present in the vocabulary prevents it to be used directly within the elassification framework.

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In this work, we aim at developing new tools to build more reliable text generators, which can be used in practical systems. First, we work in the unsupervised detection setting where we do not assume that we have access to OOD samples as they are often not available. Second, we work in the black-box scenario, which is the most common in the Software as a Service framework Rudin & Radin (2019). In the black-box setting detection methods only have access the output of the DNN architecture. Third, we want an easy-to-use and effective method to ensure adoptability. Last, we argue that OOD detection impacts on tasks specific performance of the whole system should be taken into account when choosing OOD detectors in an operational setting.
Our contributions. Our main contributions can be summarized as follows:
more operational benchmark for text generation OOD detection. We present LOFTER the Language Out oF dis pErformance benchmaRk. Existing works on OOD detection for language modeling (Arora et al., 2021) focus on (i) english language only, (ii) the GLUE benchmark and (iii) measure performance solely in terms of OOD detection. LOFTER is, in view, a more operational setting with a strong focus on neural machine translation (NMT) and dialog generation. First, it introduces more realistic data shifts that go beyond English Fan et al. (2021): language shifts induced by closely related language pairs (e.g., Spanish and Catalan or Dutch and Afrikaans ) and domain change (e.g., medical news data different types of dialogs). In addition, LOFTER comes with an updated evaluation setting: detectors' performance are jointly evaluated w.r.t the overall system's 's performance on the end task.
2. Novel information theoretic-based detectors. We present RAINPROOF: Relative informAItioN Projection Out OF distribution detector. RA RAINPROOD is fully unsupervised. It is flexible and can be applied both when reference samples (IN) are available (corresponding to scenario so) and when they are (corresponding to scenario sj). RAINPROOF tackles so by computing the models' predictions negentropy (Brillouin, 1953). For s1, it relies its natural extension: the Information Projection (Kullback, 1954; Csiszár, 1967), an information-theeretiic tool that remains overlooked by the machine learning community.
3. New insights on the operational value of OOD detectors Our extensive experiments LOFTER show that OOD detectors may filter out samples that are well processed by the model and keep samples that are not, leading lo weaker performance. Our results show that RAINPROOF breaks this curse and achieve good results in OOD detection while increasing performance.
4. Code and reproductibility. After acceptance, we will publish the open-source code on qi thub. com and the data to facilitate future research, ensure reproducibility and reduce computational costs.
2 PROBLEM STATEMENT & RELATED WORKS
2.1 NOTATIONS & CONDITIONAL TEXT GENERATION
Let us denote Ω a vocabulary of size [o] and Ω² its Kleene closure (Fletcher et al., 1990)². We denote P(2)) = p E [0, 1]
Σ [o] 1 P: = 1 the set of probability distributions defined over Q. Let Dirain be the training set, composed of N > 1 i.i.d. samples {(x', y²) E (X' x )) with probability law pxy. We denote Px and Py the associated marginal laws of pxy. Each x is sequence of tokens and we denote r E 22 the jth token of the ith sequence. x² = ( ".], e Ω² denotes the prefix of length t. The same notations hold for y.
Conditional textual generation. In conditional textual generation, the goal is to model a probability distribution (x, y) over variable-length text sequences (x.y) by finding ps R yx. for any (x,y). y). In this work, we assume to have access to pretrained conditional language model fo X x y + R where the output is the (unormalized) logits scores. fo parameterized po, i.e. for any (x, y), y)) an input sequence x, the pretrained language fo can recursively generate an output sequence ý by
softmax(fo(x ()) y)) where T E R denotes the temperature. Given
Afrikaans a daughter language of Dutch (Jansen et al., 2007). The Dutch sentence: Appelen zijn gewoonlijk groen, geel of rood can be translated in "Appels is gewoonlik groen, geel of rooi." 'he Kleene closure corresponds sequences of arbitrary size written with words in Q2 Formally: 52"
U .
k=
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sampling V6+1 p (-1x, y ye),, for t E [1, Note that do is the start of sentence (< SOS > token). We denote by S(x), the set of normalized logits scores generated by the model when the initial input is i.e., S(x) = cootmax((fo(xx fg:))) Note that elements of S(x) discrete probability distributions on f
2.2 PROBLEM STATEMENT
In OOD detection the goal is to find an anomaly score a X - R. that quantifies how much a sample is far from the IN distribution. is classified IN OUT according to the score a(x). Following previous work (Hendrycks & Gimpel, 2016), one fixes a threshold y and classifies the test sample IN if (x) 7 OOT if a(x) > 7. Formally, let us denote g(:7) the decision function, if a (x) > y
take: g(x,7)
O
i (X)
Remark 1. In our setting, OOD examples at least 80% of the train set is classified as IN data. This assumption is reasonable since, in practice, even a well tailored dataset might contains significant shares of outliers (Mishra et al., 2020).
available. In experiments, take y such that
2.3 REVIEW OF OOD DETECTORS
OOD detection for classification. Most works on OOD detection have focused detectors for classifiers and relies either on internal representations (features-based detectors) or on the final soft probabilities produced by the classifier (sofimax based detectors).
Features-hased detectors. They leverage latent representations to derive anomaly scores (Kirichenko al., 2020; Zisselman & Tamar, 2020). The most well-known is the Mahanalobis distance (Lee et al., 2018b; Ren et al., 2021b) but there are other methods employing Grams matrices (Sastry & Oore, 2020), Fisher Rao distance (Gomes al., 2022) or other statistical tests (Haroush et al., 2021). Other methods rely on the gradient space (Huang et al., 2021) the moment of the features (Quintanilha et al., 2019; Sun et al., 2021). These methods require access to the latent representations of the models, which does not fit the black-box scenario. Moreover, they often rely per-class decision, which is fine for classifiers but the sheer number of words in makes impossible to use for text generation.
Softmax- detectors. These detectors rely on the soft probabilitics produced by the model. The maximum softmax probability (Hendrycks & Gimpel, 2017; Hein et al., 2019; Liang et al., 2018; Hsu et al., 2020) uses the probability of the mode while others take into account the entire distribution, such the Energy-basee OOD detection scores (Liu al., 2020). Due to the large vocabulary size, it is unclear how these methods generalize to sequence generation tasks.
OOD detection for text generation. Little work has been done on OOD detection for text generation. Therefore, we will follow Arora et al. (2021) and will rely on their baselines but also generalize common OOD scores such as MSP or Energy to the context of text generation.
Generalization to sequence generation. We generalize common OOD detectors for classification tasks by computing the average OOD score along the sequence at each step of the text generation. We refer the reader to Sec, A.6 for more details.
Remark 2. Note that features- based detectors assume white-box framework where the internal representations an input are accessible. By contrast to softmax- based detectors which only rely on the final output. Following Arora et al. (2021), we work in black-box framework (Chen et al., 2020). We also compare our results to the Mahalanobis distance (Lee et al., 2018b), as it is known to be a strong baseline.
RAINPROOF AN INFORMATION THEORETIC OOD DETECTORS
3.1 INFORMATION THEORETICAL BACKGROUND
An information measure I P(Ω) P(2) + R quantifies the similarity between any pair of discrete distributions p. q E P(2)). Since Ω is a finite set, we will adopt the following notations p p Piay and q [q a|]- The development of new information measures for
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specific applications has received much attention over the years (Fujisawa & Eguchi, 2008; Cichocki et al., 2011) (we refer the reader to Basseville (2013) for complete review). While there exist information distances, is, in general, difficult to build metries that satisfy all the properties of distance, thus we often rely on divergences which drop the symmetry property and the triangular inequality. In what follows, motivate the information measures will use in this work.
First, we rely on the Rényi divergences (Csiszár, 1967). Rényi divergences belong to the f-divergences family and parametrized by a parameter R {1}. They are flexible and include well-known divergences such as the Kullback-Leiber divergence (KL) Kuilback (1959) (when - 1) or the Hellinger distance (Hellinger, 1909) (when 0.5). The Rényi divergence between p and q is defined as follows:
!
(a(pl) = log
(1)
The Renyi divergence widely used in machine learning (Peters et al., 2019) because a allows weighting the relative influence of the distributions' tail.
Second, we investigate the Fisher-Rao distance (FR). FR is a distance on the Riemannian space formed by the parametric distributions, using Fisher information matrix its metric (Amari, 2012). It computes the geodesic distance between two discrete distributions (Rao, 1992; Pinele et al., 2020) and is defined follows:
[
2 π
FR q)
arccos
Σ vP: x q:
(2)
1=1
It has recently found many applications (Picot et al., 2022; Colombo et al., 2022b;a) and is known to be more accurate than popular divergence measures (Costa et al., 2015).
3.2 RAINPROOF FOR THE NO- NO-REFERENCE SCENARIO (sp)
At inference time, the no-reference scenario (5n) does assume the existence of a reference set of IN samples to decide whether a new imput sample is OOD. Softmax-bar based detectors such as MSP (Hendrycks & Gimpel, 2016), Energy (Liu al., 2020) or the sequence likelihood (Arora al., 2021) are examples of OOD scores operating under 5p-
Under these assumptions, our OOD detector RAINP ROOF composed of three steps. For given input x with generated sentence y:
1. We first use fo to extract the step-by-step sequence of soft distributions S(x).
We then compute an anomaly score (x)(x)) by averaging a step-by-step score provided by I. This step-by-step score is obtained by measuring the similarity between a reference distribution E P(2) and one element of S(x). Formally:
aT(x)=
Σ
(pllu),
(3)
[S()]
DES(x)
where [8(x) |
3. The last step consists in thresholding the previous anomaly score (x)).. If az x is over a given threshold 7, we classify x as an OOD example.
Interpretation of Eq. 3. az ar(x) measures the average dissimilarity of the probability distribution of the next token to normality (as defined by u). ax(x) also corresponds to the token average uncertainty
of the model fo to generate & when the input x. The intuition behind Eq. 3 that the distributions produced by fe. when exposed to an OOD sample, should be far from normality and thus should have a high score.
Choice of and T. The uncertainty definition of Eq. 3 depends on the choice of both the reference distribution and the information measure I. A natural choice for the uniform distribution,
'The likelyhood of the sequence the same the perplexity. In our work we report the log- legllikeehhodd for 191-1 log 1o
numerical stability reasons: Le., as ax(x)= - Ei
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which we will use in this work. It is worth pointing out that Z(-Ilu) yields the negentropy of a distribution. Other possible choices for include one hot tf-idf distribution (Colombo et al., 2022b). For Z, we rely on the Rényi divergence to obtain ap. and the Fisher-Rao distance to obtain dpR-
i... u
3.3 RAINPROOF FOR THE REFERENCE SCENARIO ($1
In the with reference scenario (31). we assume that one has access to reference set of IN samples R. where [R] the size of the reference set. For example, the Mahalanobis distance works under this assumption. One of the weakness of Eq. 3 is that imposes is to imposes- an ad-hoc choice when using (the uniform distribution). $1, we can leverage RR to obtain a data-driven notion normality.
R = =xx² (x',y y²) D Dirain]:- )=
Under $1, our OOD detector RAINPROOF follows these four steps:
1. (Offlinee For each x" R, we generate yé and the associated sequence of probability distributions (S(x')). Overall we thus generate ExER ly'l probability distributions which could explode for long sequences*. To overcome this limitation, we rely on the bag of distributions of each sequence (Colombo et al, 2022b). We form the set of these bags of distributions
8" s'
Σ
S(x')
P
xiER
PeS(x))
2. (Online) For a given input x with generated sentence ý, we compute its bag of distributions representation
p(x)
p. PES(X)
Σ
(5)
Sx)
3. (Online) For x, we then compute an anomaly score a+(x) by projecting p(x) on the set S*.
Formally, oz (xx is defined as:
(x)) min T(plip(x)).
(6)
PES.
We denote p* (x) = arg pil
P
4. The last step consists of thresholding the previous anomaly score ar(x). If of is over a given threshold 7, we classify x as an OOD example.
Generalized Information Projection (Kullback, 1954; Csiszár, 1975; 1984) which measures the similarity between p(x) and the set St. Note that the closest element of S* in the sens of I can give insights the decision of the detector. It allows to interpret the decision of the detector as we will see in Tab. 5.
Interpretation of Eq. 6. (x(x) relies
Choice of I. Similarly Sec. 3.2, will rely on the Rényi divergence to define oj (x) and the Risher-Rao distance OFR (x).
RESULTS ON LOFTER
4.1 LOFTER: LANGUAGE OUT OF DISTRIBUTION PERFORMANCE BENCHMARK
LOFTER for NMT. We consider two main types of changes: language changes and domain changes, which both can occur in real-world situations. For each shift, we rely on pretrained generators from the HuggingFace Hub. Further experiemental details are relegated to Ap. A. Language shifts can
'It is also worth pointing that doing projection each timestep would require per-step reference set addition the computational time required actually compute the projections, therefore decided aggregate the probability distributions over the sequence.
'The minimization problem of Eq. 6 finds numerous connections in the theory of large deviation (Sanov, 1958) or in statistical physics (Jaynes, 1957).
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Table I: Summary of the performance and computational cost of every detector.
(a) Summary of the performance of our detectors (Ours) compared to com- monly used strong baselines (Bas.). We report in bold the best detector for each scenario and we underline the best overall.
(b) Computation time (in seconds) for the different de- tectors. Off. (Onl.) stands for offline (resp. online)
Language shifts FPR
Domain shifts FPR
Dialog shifts
time.
AURO2
AUROC
AUROC
FP8
Ours
R.D..
0.95
0.25
0.84
0.85
0.62
0.75
0.79
0.64
0.66
Score
Off.
Onl.
50
Bas.
OMSP
0g
OFR
0.87
0.89
0.93
0.28
0.44
0.75
0.77
0.83
0.74
0.76
0.78
0.87
0.77
0.60
0.71
0.66
0.72
0.70
0.72
0.76
0.64
0.21
#MSP
&D.
X
X
2.10 -3
1.10
0.78
0.50
0.72
0.65
0.95
4MM
40s
3.10 3
0ng
0.88
0.78
0.86
0.70
0.86
0.52
aD:
X
9.10-2$ s
Ours
0.88
0.35
0.77
0.81
0.69
0.69
0.76
0.75
0.38
0.92
0.73
0.40
0.35
Bus,
0g
0.71
0.80
0.62
0.68
0.76
0.67
0.72
0.61
0.48
appear when translation system is exposed to language that is extremely similar to the language the system has been trained on (e.g., Afrikaans for a system trained on Dutch) and, therefore, can lead to significant translation errors (see Tab. 7)). For language shifts, we focus on closely related language pairs coming from the Tatoeba dataset (Tiedemann, 2012b) (see Tab. 6). We study the shifts Portugese-Spaniss and Afrikaans- Dutch. Domain shifts, which occur
induced by Gatalan-Spaniste when the model is exposed to a specific topic that was not seen during training, can also affect the quality of the translation (see Tab. 4). To simulate domain shifts, use the language Tatoeba MT dataset (Tiedemann, 2020) and the news commentary dataset (Tiedemann, 201200 2b) as base datasets and the shifts are induced by the EuroParl dataset (Tiedemann, 2012a) and EMEA (Tiedemann, 2012b) dataset.
LOFTER for dialogs. For conversational agents, an interesting scenario when goal-oriented agent designed to handle specific type of conversations (e.g., customer conversations, daily dialogue) is exposed to an unexpected conversation. In this case, is crucial to interrupt the agent so it does not damage the user's 's trust with misplaced responses (Perez et al., 2022). We rely on the Multi woz dataset (Zang et al., 2020), human to human dataset collected in the Wizard-of-OF set-up (Kelley, 1984), for IN distribution data. This choice is mostly motivated by the availability of pretrained models on Multi i woz. For dialog shifts, we use spoken datasets coming from various sources which are part of the SILICONE benchmark (Chapuis et al., 2020). Specifically, oriented dataset (i.e., Switchboard Dialog Act Corpus (SwDA) (Stolcke et al., 2000)), a multi-party meetings dataset (i.e., MRDA (Shriberg et al., 2004) and Multimodal EmotionLines Dataset MELD (Poria et al., 2018)), daily communication dialogs ( i.e., DailyDialog DyDA Li et al. (2017)), and scripted scenarii (i.e., IEMOCAP Tripathi al. (2018)). We refer the curious reader to Sec. A.4 for more details on each dataset.
use goal-
Metrics. OOD detection is usually framed as unbalanced binary classification problem where the class of interest is OUT. We can assess the performance of our OOD detectors focusing on the False alarm rate (FPR) and on the True detection rate (TPR). To evaluate the performance on the OOD task we report the AUROC and the FPR.
Area Under the Receiver Operating Characteristic curve (AUROC) (Bradley, 1997). The AUROC can be interpreted as the probability that IN-distribution example has higher anomaly score than an OOD sample. For this metric, higher is better.
False Positive Rate at r% True Positive Rate (FPR). In many practical application, we have to detect at least 1% of the the OOD samples. This corresponds to pre-defined safety level. FPR quantifies the share of IN samples we wrongly detect under this constraint. It lcads to select threshold "r such that the corresponding TPR equals r. In our work r is set to 95%. Additional details on these metrics can be found in Sec. A.1. F1, precision and recall. In addition report the F1 scores of the detectors with a threshold designed such that 80% of the IN dataset is actually classified as IN
4.2 EXPERIMENTS MACHINE TRANSLATION AND RESULTS
Results on language shifts. We assess, for each language pair, the OOD detection performance of RAINP ROOF and report the average AUROC and FPR in Tab. la. We provide the detailed results in Tab. 8. We find that no-reference methods (ap, and @FR) achieve better performance that

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common no-reference baselines but also outperform the reference-based baseline. In particular, a by achieving an AUROC of 0.95 and F2R 0.25, outperforms all considered methods. Moreover, while no-reference baselines only capture up 62% of the OOD samples average, ours detect up to 83.5%, achieving even better results than the with-reference baseline (75.3%).
Results domain shifts. We eval- uate the OOD detection performance of RAINNPOOF on domain shifts in Spanish and German with technical medical data and parliamentary data. We report the average OOD detec- tion performance in Tab. la. In So. we observe that a,, and OFR outper- form the strongest baselines (i.e., En- ergy, MSP and sequence likelihood) by several AUROC points. Interest- ingly enough even no-reference detectors outperform the reference- based baseline (i.e., aM)) However, we find that relying on a reference set a must-have in terms FPR. While ap. achieves similar AUROC performance its information projection counterpart a the latter achieve much better FPR.
Table 2: Correlation between OOD scores and translation metrics BLEU and BERT-S on domain shifts datasets.
Bertscore fl
Bieu
OUT
Scenario
Score
Ours
6.2,
-0.36
-0.30
-0.27
-0.24
-0.26
-0.19
6g
-0.19
-0.24
-0.33
-0:26
-0.19
-0.39
Baselines
0:51
114
1749
-0.15
-0.19
-0.29
-0.24
-0.16
-0.37
Ours
aDa
-0.12
0.00
-0.09
-0.19
0.00
-0.09
8F3*
-0.11
0.00
-0.12
-0.17
0.00
-0.10
Baselines
de
0.02
0.00
0.06
-0.06
0.00
0.07
4.3 EXPERIMENTS IN DIALOG GENERATION AND RESULTS
Results on Dialog shifts. The dialog shifts benchmark is more difficult than NMT benchmark as all detectors achieve lower performances. It is the only case where no-reference detectors do not outperform the Mahalanobis baseline and achieve only 0.79 in AUROC. The best baseline is the Mahalanobis distance and achieves better performance on dialog task than NMT domain shifts reaching an AUROC of 0.84. However, our reference based detector based on the Rényi information projection secures better AUROC (0.86) and better FPR (0.52). Even though RAINPROOF outperforms all the baselines, shifts in dialog are hard to detect and will require further investigations. Non-aggregated results for dialog are provided in Ap. C. They show that RA ROOF consistently outperforms baselines on all datasets.
Importance of distribution tails. Our results show that, when it comes to domain shift (domain shifts in translation or dialog shifts), reference-based detectors are required to obtain good results. They also show that, the more these detectors take into account the tail of the distributions, the better they are, displayed in Sec. B.I. We find that low values of a (near 0) yields better results with the Rényi Information projection a - It suggests that the tail of the distributions used during text generation carries context in- formation and insights on the processed texts. Such results are consistent with findings recent works in the context of automatic evaluation of text generation (Colombo al., 2022b).
0.9
[R]
400
0.8-
800
AUROC
1600
0.7
2000
0.6
13
1.6
19
Comparison to the Mahalanobis distance. Our reference- based detector work with a small reference set. In our experiments, we use reference sets size 10 to 2000. The Mahalanobis distance requires to approximate the covariance matrix of the reference set. In our simula- tions, the embeddings of dimension 512 make the estima- tion unreliable. On the contrary, RA RIIINPROO ROOF, which rely on information projections, remains numerically sound with
Impact of the perfor- mance of the Rényi information projec- tion for dialog shifts detection. A smaller a increases the weight of the tail of the distribution. An of 0 would consist in counting the number of the common non zero elements.
Figure 1:
small reference
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Table Average impact of different OOD detectors on the BLEU score for different type of dataset: IN data only, OOD data and the combination of both ALL. For each we report the absolute average BLEU score (Abs.), the average gains BLEU (G.s) compared to a setting without OOD filtering (ʃo only) and the share of the subset removed by the detector (R.Sh.). These results are achieved by setting 7 such that we remove 20% of the IN dataset.
Absolate
Gnins
Removed shares
Absolate
Canns
Kemowed
Abseluze
Gains
Removed
X
31.b
+0:01
0/0P%
30,8
+00
100%
18:3
-000
0,0%
Dur
36.6
57.2
+3.1
+3.6
19.03
19:79
40.9
39.9
+10
10:1
60.88
54.6
35.6
610.3
+1882
36.7%
55:15
50
56.3
+2.7
20.0%
31.9
11.1
315'
35.3
+38
24.15
Bas.
56.1
+4.5
19.29
34.6
134
43.755
$2.4
+8.0
28.9%
31.6
91
5 TOWARDS A PRACTICAL EVALUATION OF OOD DETECTORS
Following previous work, measure the performance of the detectors the OOD detection task based on AUROC and FPR. However, this evaluation framework neglects the impact of the detector on the overall system's 's performance. We identify three main evaluation criteria that are important in practice: execution time, overall system performance in terms of quality of the generated sentences, and interpretability of the decision. Our study conducted on NMT because due the existence of relevant and widely adopted metrics for assessing the quality of a generated sentence (i.e. BLEU (Papineni al., 2002) and BERTSCORE (BERT-S) (Unanue et al., 2021)).
5.1 COMPLEXITY STUDY
Runtime and memory costs. We report in Tab. 1b the runtime of all methods. Detectors for so are faster than the ones for s₁. Contrarily to detectors using references, the no-reference detectors do not require additional memory. They can be setup easily in plug&play manner at the output of any model.
Numerical stability. The Mahalanobis distance requires to estimate both μ and Σ (see Sec, A.6). The dimension of the latent space of the considered pre-trained model is either 768 or 512. this setting, when the size of the reference set is small, the estimation of the Mahalanobis parameters is numerically unstable. For $1, RA INP ROOF relies on information projection and does not involve numerically unstable computations but requires larger memory footprint (0.5 GB) store the reference set (2000 probability distributions of dimension 50K).
5.2 IMPACT OF OOD FILTERING ON TRANSLATION QUALITY
The main objective of OOD filtering is remove samples that far from the training distribution. On these samples, the user has no guarantee that the model will produce good quality translation. In this experiment, we compare the performance of the system with and without the different detectors in terms of the quality of the generated sentence.
Global performance. Tab. 3, we report the global performance of the systems (fo) without and with OOD detectors on IN samples, OOD samples and all samples (ALL). From the first row of Tab. 3, we notice that OOD samples are harmful to the model. We observe that, in most of the cases, adding detectors increases the model performance on IN, OOD and all samples. Exceptions include aMsP (for OOD, IN and ALL.) and aw (for OOD). Results indicate that no-reference RA ROOF outperforms the reference-basee version of RAINPROOE Thus, OOD detector evaluation should consider the final task performance. Overall, it is worth noting that directly adapting classical OOD detection methods (e.g., MSP or Energy) to the sequence generation problem leads to poor results in terms of performance gains (i.e., as measured by BLEU or BERT-S). In others words, the final task does not benefit from adding classical OOD detectors.
Finer performance analysis. In Tab. 4, we report the per-shift-typss performance of fo with and without OOD detector. In Tab. observe decrease in performance in the case of language and
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Table Detailed impacts on NMT performance results per tasks (Domain- or Language-shiits) of the different OOD detectors. We present results on the different part of the data: IN data, OOD data and the combination of both, ALL. For each we report the absolute average BLEU score (Abs.), the average gains in BLEU (G.s.) compared to setting without OOD filtering (ʃo only) and the share of the subset removed by the detector (R.Sh.). We provide more detailed results on each dataset in Ap. D
Domain shifts
Langrage shifls
IN
000
ALL
000
ALL
Abs.
Ri.
Rb.
Abs.
46.9
0.0%
50.3
+0.0
0,0%
Our
50.5
+2.6
19.3 155
19:055
47.6
45.5
+4.4
+3.3
49.2
505
+4.1
715
95
63.8
53.5
+5.0
+3.1
18.79
19.8%
31.3
92.1
+150
+138
K/
14.1%
60.0
+16.
+1:0
43.99
49.5
+2.4
30.05
45.5
+2.0
17.8%
47.6
+2.5
18.9%
53.3
20.0%%
18:4
+01
66 09.
49.1
+5.3
29.4%
Bis.
50.77
13.8
19.2%
17.5
+4.3
24.2%
49.9
14.5
21.7%
05.4
15.2
19.28
21.6
13.3
63 25.2%
54,9
+11.1
36.15
Owter
45.8
19.216
33.4
40.8
12.815
59.0
13
17.7%
20.1
+1
45.7
+1.9
23.75
Ours
47.1
47.1
+0.2
+2.2
18.9%
18.9%
37.7
$7.8
-5.4
-5.5
112.7%
62.692
45.9
46.0
+0.9
411
40.76
50.9
50.9
40.6
+0.7
19.9%
19.5%
25,5
25:8
+7.1
+7.2
60.258
10005
$1.9
52.0
+8.2
48.2
55.29
55.5%
Bas.
0.5
90
47.6
46.9
+3.7
-0.0
30.08
13.85
43.5
10.3
-0.9
01.92
45.4
10.3
4100
50.9
90.4
+0.7
20.09%
15.6%
15:1
+2.0
110
50.05
46.7
90.4
+6.0
12.9
22.5%
domain shifts, the latter being more harmful. On domain shifts, we observe that reference-basee based detectors decrease system's 's performance on OOD samples. This means that the detectors tend filter out samples that are well-handled by the model and ignore sentences that are not. It is worth noting that reference- based detectors remove, in proportion, twice as many samples as their no-reference counterparts, while the threshold selection procedure remains the same. This observation also holds when removing less samples (i.e., calibrating y that we remove 10%, 5% or even 1% of the IN dataset) (Tab, 15).
Threshold free analysis. In Tab, 2, we report the correlation between OOD scores and final task performance for the case of domain shifts. We refer the reader to Tab. 14 for the results language shifts. We observe that the likelihood score is the most correlated with the final sentence quality, as measured by BLEU BERT-S. This finding illustrates that higher correlation with sentence quality does not necessarily translate into higher performance gains when filtering OOD samples. This resuls suggests that Quality Estimation (Specia al., 2010; Blatz al., 2004), while closely related, is different problem.
5.3 TOWARDS AN INTERPRETABLE DECISION
An important dimension fostering adoption is the abil- ity to verify the decision taken by the automatic system (Montavon et al., 2018). RAINPROOF offers a step in this direction when used with references: for each input sample, RA RAINPROOS finds the closest sample (in the sens of the Information Projection) in the reference set to take its decision. We present in Tab. 5 some OOD samples along with their translation scores, projection scores, and their projection on the reference set. We notice that, in general, sentences that are close to the reference set, and whose projection has a close meaning, are better handled by fe. Therefore, one can visually interpret the prediction of RIIN ROOF, and validate it. This observation further validate our method.
Table 5: OOD inputs, their translations and projections onto the reference set. The first are far from the reference set and not well translated whereas the next 2 are very close the reference set and well translated. We can, for that matter, notice that the projection is quite close to the input sentence grammatically speak- ing.
Samin
Alice a Asril siiees soiuillr See lns
Goood suas
Lar sight spoiled audi t0puat
p"(s)
gate per
Soaco
Aquara cala diussea an
giar
p*0x)
Esto cachaa
Soore
6 CONCLUSIONS
saaca
Agurtu di sascaat unch eronga
Thes
tehice
Gorwad
This
p'(s)
In this work, we introduced both a detection framework called RAINPROOF as well new benchmark called LOFTER for detecting OOD samples when using textual generators in the black-box scenario. Our work adopts an operational perspective by not only considering OOD performance but also task-specific metries. Our results show that, despite the good results obtained in
innvo
Gooed nut
Aueriiiia Asaria
p"0o
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pure OOD detection, OOD filtering can harm the performance of the final system, as it is the case for MSP or Mahanalobis. We found that,
breaks this curse and induces significant gains in translation performance both OOD samples and in general. In conclusion, this work paves the way to the development of detectors tailored for text generators and calls for a global evaluation when henchmarking future OOD detectors.
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REFERENCES
Shun-ichi Amari. Differential-geomeriici methods in statistics, volume 28. Springer Science & Business Media, 2012.
Alessandro Antonucci, Alessandro Facchini, and Lilith Mattei. Structural learning of probabilistic sentential decision diagrams under partial closed-world assumption. 2021. doi: 10.48550/ARXIV.
2107.12130. URL https: aawiiiiiiivv/...ia/aaaaaaaaaavvv wwwpg//bs/21111 12130.
Udit Arora, William Huang, and He He. Types of out-of-distribution texts and how to detect them. arXiv preprint arXiv:2109.0683 2021.
Michéle Basseville. Divergence measures for statistical data processing an annotated bibliography, Signal Processing, 93(4):621-633, 2013.
John Blatz, Erin Fitzgerald, George Foster, Simona Gandrabur, Cyril Goutte, Alex Kulesza, Alberto Sanchis, and Nicola Ueffing. Confidence estimation for machine translation. In Coling 2004: Proceedings of the 20th international conference on computational linguistics, pp. 315-321, 2004.
Andrew P Bradley. The use of the area under the curve in the evaluation of machine learning algorithms. Pattern recognition, 90(7): 1997.
L. Brillouin. The negentropy principle of information. Journal of Applied Physics, 24(9):1152- 1163, September 1953. doi: 10.1063/1.121166. URL https: /ttp:///ooio//.oo htt//oi.ogggg////// 1063/1. 1721463.
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et Language models are few-shot learners. Advances in neural information processing systems, 33:1877-1999 2020.
Emile Chapuis. Pierre Colombo, Matteo Manica, Matthicu Labeau, and Chloé Clavel. Hierar- chical pre-training for sequence labelling in spoken dialog. Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 2636-2648, Online, November 2020. As- sociation for Computational Linguistics. doi: 18. fi220000inninggeemmnlp.. https: /ttp:://wwwww wwwweblwbb........... org/ httpg//nhhllgy//22000 http://222....ninnooo..o findings emn 239.
URL
Jianbo Chen, Michael I Jordan, and Martin J Wainwright. Hopskipjumpattack: A query-efficient decision-based attack. In 2020 ieee symposium on security and privacy (sp), pp. 1277-1294. IEEE, 2020.
Andrzej Cichocki, Sergio Cruces, and Shun-ichi Amari. Generalized beta divergences and their application to robust nonnegative matrix factorization. Entropy, 13(1):134-170, 2011.
Pierre Colombo, Guillaume Staerman, Nathan Noiry, and Pablo Piantanida. Learning disentangled textual representations via statistical measures of similarity. arXiv preprint 20X.:22205.03589 2022a.
Pierre Jean Colombo, Chloé Clavel, and Pablo Piantanida. Infolm: A new metric to evaluate summarization & data2text generation. In Proceedings of the AAAI Conference Artificial Intelligence, volume 36, pp. 10554-10562, 2022b.
Sueli IR Costa, Sandra A Santos, and Joao E Strapasson. Fisher information distance: geometrical reading. Discrete Applied Mathematics, 197:59-69 69, 2015.
Imre Csiszár. Information-tyy measures of difference of probability distributions and indirect
observation. studia scientiarum Mathematicarum Hungarica, 2:229-318, 1967.
Imre Csiszár. I-divergence geometry of probability distributions and minimization problems. The annals of probability, pp. 146-158. 1975.
Imre Csiszár. Sanov property, generalized i-projection and conditional limit theorem. The Annals of Probability, pp. 768-793, 1984.
Jesse Davis and Mark Goadrich. The relationship between precision-recall and roe curves. In Proceedings of the 23rd international conference Machine learning, pp. 233-240, 2006.

---

Under review as a conference paper at ICLR 2022
Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary. et al. Beyond english- centric multilingual machine translation. J. Mach. Learn. Res., 22(107):1-48, 2021.
Geli Fei and Bing Liu. Breaking the closed world assumption in text classification. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 106,514, 2016.
Peter Fletcher, Hughes Hoyle, and C Wayne Patty. Foundations of discrete mathematics. Brooks/Cole, Florence, KY, November 1990.
Hironori Fujisawa and Shinto Eguchi. Robust parameter estimation with small bias against heavy contamination. Journal of Multivariate Analysis, 99(9)22053-2088 2008.
Eduardo Dadalto Camara Gomes, Florence Alberge, Pierre Duhamel, and Pablo Piantanida. Igeood: An information geometry approach to out-of-distributioo detection. arXiv preprint arXiv:2203.07798, 2022.
Matan Haroush, Tzviel Frostig, Ruth Heller, and Daniel Soudry. A statistical framework for efficient out of distribution detection in deep neural networks, 2021.
Matthias Hein, Maksym Andriushchenko, and Julian Bitterwolf. Why relu networks yield high- confidence predictions far away from the training data and how to mitigate the problem. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 41-50, 2019.
Ernst Hellinger. Neue begründung der theorie quadratischer formen von unendlichvielen veränderlichen. Journal für die reine und angewandte Mathematik, 1909(136)210-271,, 1909.
Dan Hendrycks and Kevin Gimpel. A baseline for detecting misclassified and out-of-distribution examples in neural networks. arXiv preprint 161iv:1610.0//:1 2016.
Dan Hendrycks and Kevin Gimpel. A baseline for detecting misclassified and out-of-distribution examples in neural networks. In International Conference on Learning Representations, 2017.
Yen-Chang Hsu, Yilin Shen, Hongxia Jin, and Zsolt Kira. Generalized odin: Detecting out-of- distribution image without learning from out-o--distribution data. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pp. 10951-10960, 2020.
Haiwen Huang, Zhihan Li, Lulu Wang, Sishuo Chen, Bin Dong, and Xinyu Zhou. Feature space singularity for
detection. arXiv preprint arXiv:2011.14654 2020.
Rui Huang, Andrew Geng, and Yixuan Li. On the importance gradients for detecting distributional shifts in the wild. ArXiv, abs/21 10.00218, 2021.
Carel Jansen, Robert Schreuder, and Anneke Neijt. The influence of spelling conventions on perceived plurality in compounds: A comparison of afrikaans and dutch. Written language & literacy, 10(2): 185-194, 2007.
Edwin Jaynes. Information theory and statistical mechanics. Physical review, 106(4):620, 1957.
John F Kelley. An iterative design methodology for user-friendly natural language office information applications. ACM Transactions on Information Systems (TÓIS), 2(1):26-41, 1984.
Polina Kirichenko, Pavel Izmailov, and Andrew G Wilson. Why normalizing flows fail to detect out-of-distribution data. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and Lin (eds.), Advances in Neural Information Processing Systems, volume 33, pp. sociates, Inc., 2020. URL httpttttppp//ppccceeeeeeee www.iinggn..uuureeeeeeeeee.... neurips oc paper/ 2020/ /file/ pdf.
Curran As-
ccb9fe2fbbe 9c339c11135300 wtw.eeee1111166882228888883338
Solomon Kullback. Information theory and statistics. Courier Corporation, 1954.
Solomon Kullback. Information Theory and Statistics. John Wiley, 1959.
12

---

Under review as a conference paper at ICLR 2022
Kimin Lee, Kibok Lee, Honglak Lee, and Jinwoo Shin. A simple unified framework for detecting out-of-distribution samples and adversarial attacks. Advances in neural information processing systems, 31, 2018a.
Kimin Lee, Kibok Lee, Honglak Lee, and Jinwoo Shin. A simple unified framework for detecting out-of-distribution samples and adversarial attacks. In S. Bengio, H. Wallach, H. Larochelle, and R. Garnett (eds.), Advances in Neural Information Processing Curran Associates, Inc., 2018b.
K. Grauman, N. Systems 3/, pp- 7167-7177.
Kimin Lee, Kibok Lee, Honglak Lee, and Jinwoo Shin. A simple unified framework for detecting out-of-distribution samples and adversarial attacks, 2018c. URL https: http://wwwwv.....oo.o... abs/
Yanran Li, Hui Su, Xiaoyu Shen, Wenjie Li, Ziqiang Cao, and Shuzi Niu. Dailydialog: A manually labelled multi-tumn dialogue dataset. In Proceedings of The 8th International Joint Conference on Natural Language Processing (IJNLP 2017), 2017.
Shiyu Liang, Yixuan Li, and R. Srikant. Enhancing the reliability of out-of-distribution image detection in neural networks. In International Conference on Learning Representations, 2018. URL https:tpp/// / /tpp///ppeeeeeeeeeeeeeeeeeeeeeee wwt.iiiwweeeeeeeeeeeee net/forum? d= TxRZ.
Weitang Liu, Xiaoyun Wang, John Owens, and Yixuan Li. Energy- of-distribution detection. Advances in Neural Informaiion Processing Systems, 2020.
Yifei Ming, Yiyou Sun, Ousmane Dia, and Yixuan Li. Cider: Exploiting hyperspherical embeddings for out-of-distribution detection. arXiv preprint arXiv:2203.04450, 2022.
Swaroop Mishra, Anjana Arunkumar, Bhavdeep Sachdeva, Chris Bryan, and Chitta Baral. Dqi: Measuring data quality in nlp. 2020. URL httpt::tt//aatt httpe/iwvvvrrrra/rree/ee http//b//20000 00816.
Grégoire Montavon, Wojciech Samek, and Klaus-Robert Müller. Methods for interpreting and understanding deep neural networks. Digital signal processing. 73:1-15, 2018.
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. Bleu: method for auto- matic evaluation of machine translation. In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311-318, Philadelphia, Pennsylvania, USA, July 2002. Association for Computational Linguistics. doi: 10.31 10.3315310779883.777333... URL https: httt:s://aclaatho..o
org/Po2-104D
Jitendra Parmar, Satyendra Singh Chouhan, Vaskar Raychoudhury, and Santosh Singh Rathore. Open-world machine learning: Applications, challenges, and opportunities, 2021. URL https:
//arxiv. org. aba abt.///ab/222......./ 2105.13448..
Ethan Perez, Saffron Huang, Francis Song, Trevor Cai, Roman Ring, John Aslanides, Amelia Glaese, Nat McAleese, and Geoffrey Irving. Red teaming language models with language models. arXiv preprint arXiv:2202.032883 2022.
Ben Peters, Vlad Niculae, and André FT Martins. Sparse sequence-to-setee tosseuence models. arXiv preprint 19Xiv:1905.05770 2019.
Marine Picot, Francisco Messina, Malik Boudiaf, Fabrice Labeau, Ismail Ben Ayed, and Pablo Piantanida. Adversarial robustness via fisher-rao regularization. IEEE Transactions Pattern Analysis and Machine Intelligence, 2022.
Julianna Pinele, João E. Strapasson, and Sueli I. Costa. The fisher rao distance between multivari- ate normal distributions: Special cases, bounds and applications. Entropy, 22(4), 2020. ISSN 1099- 4300. doi: 10.3390/e22040404, URL https://///.......t.. : wwww. wwwpiidppmmmmommmm... com 0900099-40 000999433022222200
Soujanya Poria. Devamanyu Hazarika, Navonil Majumder, Gautam Naik, Erik Cambria, and Rada Mihalcea. Meld: A multimodal multi-party dataset for emotion recognition in conversations. arXiv preprint arXiv: 118.0250000 2018.
13

---

Under review as a conference paper at ICLR 2022
Igor M. Quintanilha, Roberto de M. E. Filho, José Lezama, Mauricio Delbracio, and Leonardo O. Nunes. Detecting out-of-distribution samples using low-order deep features statistics, 2019. URL
wttpl/ewwwweeeeeeeeeeeeeeeeeeeee forum? waw.mmiddddkaka.t......
Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al. Improving language understanding by generative pre-trainn pre-training. 2018.
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, llya Sutskever, et al. Language models are unsupervised multitask learners. OpenAl blog, 1(8):9, 2019.
C Radhakrishna Rao. Information and the accuracy attainable in the estimation of statistical parame- ters. In Breakthroughs in statistics, pp. 235-247. Springer, 1992.
Jie Ren, Stanislav Fort, Jeremiah Liu, Abhijit Guha Roy, Shreyas Padhy, and Balaji Lakshmi- narayanan. A simple fix to mahalanobis distance for improving near-ood detection. arXiv preprint arXiv:2106.09022, 2021a.
Jie Ren, Stanislav Fort, Jeremiah Liu, Abhijit Guha Roy, Shreyas Padhy, and Balaji Lakshminarayanan. A simple fix to mahalanobis distance for improving near-ood detection, 2021b.
Cynthia Rudin and Joanna Radin. Why are we using black box models in ai when we don't need to? lesson from an explainable ai competition. 2019.
Seonghan Ryu, Seokhwan Kim, Junhwi Choi, Hwanjo Yu, and Gary Geunbae Lee. Neural sentence embedding using only in-domain sentences for out-of-domain sentence detection in dialog systems. Pattern Recognition Letters, 88:26-33 32, 2017.
Ivan N Sanov. On the probability of large deviations of random variables. United States Air Force, Office of Scientific Research, 1958.
Chandramouli Shama Sastry and Sageev Oore. Detecting out-of-distributtin examples with Gram matrices. In Hal Daumé III and Aarti Singh (eds.), Proceedings of the 37th International Conference on Machine Learning, volume 119 of Proceedings of Machine Learning Research, pp. 8491-8501. URL https: /tt:://prrooce http///oceeddeeeeoooooooo ngs. mlr press / /v11v111 19/ sastry20a.
PMLR, 13-18 Jul 2020.
html.
Elizabeth Shriberg, Raj Dhillon, Sonali Bhagat, Jeremy Ang, and Hannah Carvey. The icsi meeting recorder dialog act (mrda) corpus. Technical report, INTERNATIONAL COMPUTER SCIENCE INST BERKELEY CA, 2004.
Lucia Specia, Dhwaj Raj. and Marco Turchi. Machine translation evaluation versus quality estimation. Machine translation, 24(1):39-50 2010.
Andreas Stolcke, Klaus Ries, Noah Coccaro, Elizabeth Shriberg, Rebecca Bates, Daniel Jurafsky, Paul Taylor, Rachel Martin, Marie Meteer, and Carol Van Ess-Dykema. Dialogue act modeling for automatic tagging and recognition of conversational speech. Computational Linguistics, 26(3): 339-371, 2000.
Yiyou Sun, Chuan Guo, and Yixuan Li. React: Out-of-diitribution detection with rectified activations. ArXiv, abs/2111.127991 2021.
Jorg Tiedemann. Parallel data, tools and interfaces in opus. In Nicoletta Calzolari (Conference Chair), Khalid Choukri, Thierry Declerck, Mehmet Ugur Dogan, Bente Maegaard, Joseph Mariani, Jan Odijk, and Stelios Piperidis (eds.), Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC' 12), Istanbul, Turkey, may 2012a. European Language Resources Association (ELRA). ISBN 77882-9511440--7.1
Jörg Tiedemann. The Tatoeba Translation Challenge Realistic data sets for low resource and multilingual MT. In Proceedings of the Fifth Conference Machine Translation, pp. 1174- 1182, Online, November 2020. Association for Computational Linguistics. URL https:/ /www. acl web. org/ antho ogy/ 2202 wmt .139.
14

---

Under review as a conference paper at ICLR 2022
Jörg Tiedemann and Santhosh Thottingal. OPUS-MT World. In Proceedings of the 22nd Annual Conferenec of the European Association for Machine Translation (EAMT), Lisbon, Portugal, 2020.
Building open translation services for the
Jörg Tiedemann. Parallel data, tools and interfaces in opus. In Nicoletta Calzolari (Conference Chair), Khalid Choukri, Thierry Declerck, Mehmet Ugur Dogan, Bente Maegaard, Joseph Mariani, Jan Odijk, and Stelios Piperidis (eds.), Proceedings of the Eight International Conference on Language Resources and Evaluation 12), Istanbul, Turkey, may 2012b. European Language Resources Association (ELRA). ISBN 978-2-9517408-7-77
Samarth Tripathi, Sarthak Tripathi, and Homayoon Beigi. Multi-modal emotion recognition on iemocap dataset using deep learning. arXiv preprint arXiv:1804.05778. 2018.
Inigo Jauregi Unanue, Jacob Parnell, and Massimo Piccardi. Berttune: Fine-tuning neural machine translation with bertscore, 2021. URL https::t///// pettps://axxvv.. wwt./bo//////ooooooo/ooooae a21/ 02208.
Sachin Vernekar, Ashish Gaurav, Vahdat Abdelzad, Taylor Denouden, Rick Salay, and Krzysztof Czarnecki. Out-of-distribution detection in classifiers via generation, 2019a. URL https:
//axxxi..rr/abb//110000/ 04241.
Sachin Vernekar, Ashish Gaurav, Taylor Denouden, Buu Phan, Vahdat Abdelzad, Rick Salay, and Krzysztof Czarnecki. Analysis of confident-classifien for out-of-distribution detection. arXiv preprint arXiv: /904. /2220, 2019b.
Weizhe Yuan, Graham Neubig, and Pengfei Liu. Bartscore: Evaluating generated text as text generation, 2021. URL https: /ttp/axxxvvv.r.../.. 2bbs/220..1122.
Xiaoxue Zang, Abhinav Rastogi, Srinivas Sunkara, Raghav Gupta, Jianguo Zhang, and Jindong Chen. Multiwoz. 2.2: dialogue dataset with additional annotation corrections and state tracking baselines. In Proceedings of the 2nd Workshop on Natural Language Processing for Conversational AI, ACL 2020, pp. 109-117, 2020.
Rowan Zellers, Ari Holtzman, Hannah Rashkin, Yonatan Bisk, Ali Farhadi, Franziska Roesner, and Yejin Choi. Defending against neural fake news. Advances in neural information processing systems, 32, 2019.
Jingqing Zhang, Yao Zhao, Mohammad Saleh, and Peter Liu. Pegasus: Pre-training with extracted gap-sentences for abstractive summarization. International Conference on Machine Learning. pp. 11328-11339, PMIR, 2020.
Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, and Bill Dolan. Dialogpt: Large-scabe generative pre-training for conversational response generation. arXiv preprint arXiv:1911.0055 2019.
Zhi-Hua Zhou. Open-envirooment machine learning. National Science Review, 98): 123, 2022.
Qiuyu Zhu, Guohui Zheng, and Yingying Yan. Effective out-of-distrribttio detection in classifier based on pedcc-loss, 2022. URL https: wttpiiivvvvv////i///aa////aaavva org/ abs////////// abs/2204 2204 04665.
Ey Zisselman and Aviv Tamar. Deep residual flow for out of distribution detection. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2020.
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7 APPENDIX
A Experimental setting
17
A.I Additionnal Details on Metrics
17
A.2 Language pairs
17
A.3 Samples
18
A.4 Dialog datasets
18
A.5 Choices of models.
18
A.6 Generalization of existing OOD detectors to Sequence Generation
18
Parameters tuning
19
B.1 Impact of a
19
Performance of our detectors OOD detection
20
C.1 Summary of our results
20
C.2 Detailed results of OOD detection performances
20
C,3 ROC AUC curves
21
D NTM performance
24
D.I Absolute performances
24
D.2 Gains
27
D.3 Effect of Larger threshold on NMT performance
27
Negative results
27
E.1 Different aggregation of OOD metrics
................................
27
E.2 Negentropy of bag of distributions
27
E.3 Different reference distributions
28
E,4 Impact of additional finetuning on IN data
28
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Under review as a conference paper at ICLR 2022
A EXPERIMENTAL SETTING
In this section we dive into the details and definitions of our experimental setting. First we present OOD detection performance metries (Sec. A.1), then provide couple samples for one of the small language shifts (Sec. A.3). We also discusse the choices of pretrained model (Sec. A.5) and how we adapted common OOD detectors the text generation case (Sec. A.6).
A.1 ADDITIONNAL DETAILS METRICS
OOD Detection is usually an unbalanced binary classification problem where the class of interest OUT. Let us denote Z the random variable corresponding actually being out of distribution. We can assess the performance of our OOD detectors focusing on the False alarm rate and on the True detection rate. The False alarm rate or False positive rate (FPR) is the proportion of samples missclassified as OUT. For score threshold 7 have FPR Pr (a(x) > y Z 0). The True detection rate or True positive rate (TPR) is the proportion of OOD samples that are detected by the 1).
method. It is given by TPR Pr (a(x)
In order to evaluate the performance of our methods we will focus and report mainly the AUROC and the FPR, provide more detailed metries and experiments in Sec. A.1.
Area Under the Receiver Operating Characteristic curve (AUROC) Bradley (1997). The Receiver obtained by plotting the True positive rate against the False positive rate. The area under this curve is the probability that an in-distribution example Xin has anomaly score higher than OOD sample Xout AUROC= Pr(a(x,,) > a(xout)). It is given by Z 0), Pr (a(x) 7 = 1)).
Operating Characteristic curve is
y (Pr (a(x)
False Positive Rate at 95% True Positive Rate (FPR). We accept to allow only a given false positive rate corresponding to a defined level of safety and we want to know what share positive samples we actually catch under this constraint. It leads to select threshold "/r such that the corresponding TPR equals т. At this threshold, one then computes: Pr(a(x) > Yrr 1 = 0) with "r s.t. TPR(*)) = is chosen depending of the difficulty of task at hand and the required level of safety.
For the sake of brevity we present only AUROCand FPRmetrics in aggregated results but also used Detection error and Area Under the Precision- Recall curve metrics and those are presented in full results section (Ap. C).
Detection error. is simply the probability of miss-classification for given True positive rate.
Area Under the Precision-Recall curve (AUPR-IN/AUPE OUT) Davis & Goadrich (2006). The DrecisionRRecal curve plots the recall (true detection rate) against the precision (actual proportion of OOD amongst the predicted OOD). The area under this curve 7 + (Pr (2 1 s(X &
7), Pr (s(X)= & 7 Z = 1)) captures the trade-off between precision and recall made by the model. A high value represents high precision and a high recall i.e. the detector captures most of the positive samples while having few False positive.
A.2 LANGUAGE PAIRS
Model
IN data
OUT data
Language shift
DE-EN
Taloeba DE
Newa FR
DE-EN
Tatceba DE
Tatoeba NLD
ES-EN
Tatoeba
ES-EN
Tatceba ES
Tatoeba CAT
ES-EN
Tatceba ES
Tatoeba POR
NLD-EN
Tatceba ES
AFR
Domain shift
DE-EN
Talceba DE
EMEA DE
DE-EN
Tatceba
Eurparl
ES-EN
Tatoeba ES
EMEA DE
ES-EN
Talceba ES
Eurparl DE
Table 6: Summary of models and studied shifts.
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A.3 SAMPLES
Scurce sentoree
Fspartal trinslationn
Translatin
BI.RU
en Tor sgrads tecnelogis.
Tom likes technology
Tom is likes technology.
42.73
Aci està teua bossa.
Here you bag.
Aci está la tesa bess2.
8.12
Also el posar2 en penil.
That ll put you in danger.
Aiso posurá en perill.
8.12
Londies bi han mells pares bonics.
There are masy beanitul parks in London.
To London hi has molts parcs bonics.
6.57
Aquest és molt deliciós.
This bread is very delicions.
Aquest és molt deliciés.
8.12
tets els mass serics elk agnslon dis videojoct.
All frieule like playing viloogamex
tots els mans amics cls agradl els videojoes.
4.20
This Eis.
0.00
Moltes felicitats!
Congratubatons!
Molles congrats!
27.52
Happy New Year!
nou!
15:97
Aspall que rebará. dos quarts set.
will lif, will steal.
mindless,, be qui clan.
12.22
I'm the
5.69
surt treballar
sold me that 6:30 every moening.
leaves
sum pull linuse was haunted.
two quarts
2.67
n'tt dit que seva cru ensbruikada. Aquest iloc on obtuer men pare.
hounted. the place where my tather bora.
27.78
Aquest is lloe on netxer mes pare.
5.30
Table 7: Example of behavior of language model trained to handle Spanish inputs on Catalan inputs.
A.4 DIALOG DATASETS
Switchboard Dialog Act Corpus (SwDA) is corpus of telephonic conversations. The corpus provides labels, topic and speaker information (Stolcke et al., 2000).
ICSI MRDA Corpus (MRDA) contains transcript 75h of naturally occuring meetings involving more than 50 people (Shriberg et al., 2004).
DaylyDialog Act Corpus (DyDA) contains daily common communications between people, covering topic such as small talk, meteo or daily activities (Li et al., 2017).
Interactive Emotional Dyadic Motion Capture JEMOCAP)(Tipaale et al., 2018) consists of transcripts of improvisations or scripted scenarii supposed to outline the expression of emotions.
A.5 CHOICES OF MODELS
lot of pretrained model for conditional text generation are available. To perform our experiments we needed models that were already well installed and deployed and that would also support OOD settings. For translation tasks we needed specialized models for a notion of OOD to be easily defined. It would be indeed more hazardous to define a notion of OOD language when working with multilingual model. The same is true for conversational models.
Neural Machine Translation model. We benchmark our OOD method on translation models provided by Helsinky NLP Tiedemann & Thottingal (2020) on several pairs of languages with large and small shifts. We extended the experiment to detect domain shifts. These models are indeed specialized in each language pairs and are widely recognize in the neural machine translation field. For our experiments we used the testing set provided along these models, so we can consider that they have been fine tuned over the same distribution.
Conversational model. We used a dialogGPT Zhang et al. (2019) model fine-tuned on the Multi WOZ dataset as chat bot model. The finetuning on daily dialogue type tasks ensure that the model is specialized, thus allowing us to get good definition of samples not being in range of expertise. Moreover, the choice of the architure, DialogGPT, guarantee that our results are valid on a very common architecture.
Additional finetuning. We further finetuned the models the reference set to check whether additional finetuning on the distribution would affect the results. It did not change significantly the results Tab. 17. It is not surprising considering that the models we used were already trained very similar distribution.
A.6 GENERALIZATION OF EXISTING OOD DETECTORS TO SEQUENCE GENERATION
In this section, we extend classical OOD detection score to the conditional text generation settting. Common OOD detectors were built for classification tasks and we need to adapt them to conditional text generation. Our task can be viewed as a sequence of classification problems with a very large number of classes (the size of the vocabulary). We chose the most naive approach which consists of
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Under review as a conference paper at ICLR 2022
averaging the OOD scores the sequence. We experimented with other aggregation such the min/max or the standard deviation without getting interesting results.
Likelihood Score The most naive approach to build OOD score is to rely solely the log- likelihood of the sequence. For a conditioning x we define the log-likelyhood score by az (x())
- ( (..)). The likelihood is the same the perplexity.
Average Maximum Softmax Probability score The maximum softmax probability Hendrycks & Gimpel (2017) takes the probability of the mode of the categorical distribution score of OOD. We extend thise definition in the case of sequence of probability distribution by averaging this score along the sequence. For given conditioning x, define the average MSP score (x)
max Pd (1)x, While it is closely linked uncertainty measures y r= =E][0,K|]
discards most of the information contained in the probability distribution. It discards the whole probability distribution. We claim that much more information can be retrieve by studying the whole distribution.
Average Energy score We extend the definition of the energy score described in Liu et al. (2020) to sequence of probability distributions by averaging the score along the sequence. For given conditioning and temperature T define the average energy of the sequence:ag(x) & b9 M |y| log E efo(x& corresponds to the normalization term of the softmax function applied on the logits. While it takes into account the whole distribution, it only takes into account the amount of unormalized mass before normalization without attention how this mass is distributed along the features.
T
Mahalanobis distance Following Lee al. (2018c) compute the Mahalanobis matrice based on the samples of given reference set R. In our case we are using encoder-decoder models we use the output of the last hidden layer of the encoder as embedding. Let's denote Φ(xx this embedding for conditionning x. Let's μ and E be respectively the mean and the covariance of these embedding on
the reference set. We define (x) (1 + (o(x) 4)) Σ (6(x) u1)
B PARAMETERS TUNING
Detectors depend on their anomaly score make decision and these scores can be parametric. First of all, soft probability based scores depend on the soft probability distribution and its scaling, therefore the temperature is crucial parameter to tune to get the most performance. While a small temperature tend to make the distribution more picky, higher value spread the probability mass along the classes. Moreover, the renyi divergence and its related informaiton projection depend on a factor a. We provide here further results and analysis of those parameters our results.
B.1 IMPACT OF
Indeed, in Fig. we present the impact of the size of the reference set and of the paramet a on Renyi information projection to distinguish dialog shifts, as expected, the larger the reference set, the better. However, we see that smaller values of yield better results.
We recall that the Renyi divergence is defined as (pll +يب log ΣΣ 2
where a E R, {1}. Smaller values of distribute the weight of each feature more equally in the final divergence, more specifically they tend to give an equal weight to the very likely outcome as well as to the less likely ones, therefore giving more weight to the tail of the distribution. When tends to 0 the Renyi divergence actually counts the number of nonzero common probabilities. That makes sens in terms of topic detection, it counts the common tokens considered during text generation.
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Under review as a conference paper at ICLR 2022
Task - Language shifts
Task - Domain shifts
Task - Dialog shifts
Ours
141
Baselines
S core
ab.
AUROC
ara
ag
a
033
a0
21R"
35
Figure Trade-offs between AUROCand FPRfor each tasks and metrics
1.0
spa-cat
spa-fr
spa
1.0
0.0
0.2
0.4
0.6
0.8
0.0
0.2
0.6
0.8
1.0
0.2
0.4
0.6
0.8
42
Figure 3: ROCAUC curves for uncertainty based metrics compared to common baselines for language shifts detection. Baselines are represented in dashed lines.
C PERFORMANCE OF OUR DETECTORS IN OOD DETECTION
C.1 SUMMARY OF RESULTS
In Fig. present the different level of performance of all the detectors studied. We can see that in every task our detectors outperform the baselines but also that in dialog shift, while the Mahalanobis distance outperform clearly our detectors for so. they still outperform baselines for their scenario by far.
C.2 DETAILED RESULTS OOD DETECTION PERFORMANCES
In this section present the performances of OOD detectors each detailed tasks, f.e. for each pair IN and OOD data with all the considered metrics. We show that our metrics outperform other OOD detectors baselines in almost all scenarios.
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Under review as a conference paper at ICLR 2022
Table 8: Detailed results of the performances of our OOD detectors different language shifts. The first language of the pair is the reference language of the model and the second one is the studied shift.
RIPEHOTT
KEWOO
FAN
PAY
preciee
Dan
055
6.99
12:09
8.55
8:00
0/55
0.33
0.80
0.56
8.41
8.45
616
E36
0.24
Rookers
039
0.71
0170
8:43
0351
0:15
0363
Reac
038
0.11
0.27
8.66
063
0186
00.00
1.68
1.30
1730
1301
1030
133
1337
033
071
103
1010
1330
1011
1370
08.00
1096
833
1199
030
033
0.75
6643
1960
1381
1056
0.43
0.27
1971
Dan
Clark
1.80
0.90
1.00
8.83
888
808
0.3
0.99
51
0.70
6.3
006
0.99
0770
1.59
57
0.50
89
83
5318
(000
0130
0.50
51
8.1
076
0.31
0.85
0653
1333
11
117
1001
Spctecc
000
111
1771
(a) deu-fr
(h) spa-por
See
Scewne
Sove
Dan
0.85
6896
1891
8:34
6:31
0643
6:19
1.81
1.0
1.89
1.89
000
0091
0.81
(053
1.88
11300
1337
075
1335
175
10111
1050
1751
1050
1750
1.55
03:55
1006
1.90
015
1.55
010
0081
-280
1000
1.88
0.76
002
0.34
0.45
0.54
3.53
0.55
602
(000
0.89
1000
1.59
0.30
008
0231
0.4
8.68
0.80
0.76
8:50
843
(90
9.61
1.83
1.80
000
091
8.80
1483
1.88
08'06
016
8:
5.33
010
(100
0385
1481
1.88
015
0.80
1199
0.00
0.40
010
(18)
0183
1654
1.55
0.53
1271
0.79
03.0
038
0113
1000
0.99
1000
(c) spa-cat
(d) spa-fr -fa
A280 3M
2025, 037
AIROC
250
1.
728,
AUDS 33
A238 20T
AUS0C
E35
01
15:30
Oen
03:55
1155
70u
0.59
8.99
0116
0011
02.55
1100
1.88
1.17
Baactack
08:03
0053
1.80
Bextac
6728
3.54
0.40
0.35
1771
8:22
1030
133
0357
110
1.80
1.80
1001
1001
0.91
1001
1300
drg
0.76
0.32
0.58
0.55
006
0.87
080
0.30
01:0
892
E40
(096
610
0190
3.55
0.5
000
1000
Opt
0.55
0130
8:48
006
1190
3.57
0.99
000
(00)
0.8
1000
8.99
006
1190
0327
014
1036
1393
Bockess
1000
1227
Socters
084
122
177
1001
(e) nld-afi
(1) deu-nl
C.3 ROC AUC CURVES
C.3.1 LANGUAGE SHIFTS
deu-fr
deu-nl
rid-afr
6.4
0.2
6.0
spe-cat
spa-fr
spe-por
6.4
6.2
6.0
0'4
1.0
0.4
3ra"
307 A0;
AM
30g
Figure 4: ROC-AUC curves for our reference based metries compared to common baselines for language shifts detection. Baselines represented in dashed lines.
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Under review as a conference paper at ICLR 2022
Table Detailed results of the performances of our OOD detectors on different domain shifts. For Spanish (spa) and German (de), we present two domains shifts: Technical medical (EMEA) data and legal parlementary texts (parl) against common language emboddied by the Tatoeba dataset (tat).
KEWOO
AUNTCC
preciee
Dat
0.75
03:1
6.6
0:11
0.06
0.16
0.35
0.66
0.33
0.30
0144
0.45
0.81
01/11
1.55
Bookees
0.59
0.73
1.68
636
(191
0:33
0130
Reac
0.48
0.11
0.68
0148
13:3
04.30
00
11477
1.93
11:00
1230
0.2
132
1075
1130
1750
175
107
100
1007
01%
3.29
0888
1090
1381
030
011
6643
063
0230
1000
0.99
0.41
1000
Dan
Clark
0.57
0.73
0.83
8.88
531
091
0.38
0.55
0106
3.55
8.00
0.15
000
0.85
0000
1.00
53
0.53
0.53
89
033
090
0.13
0335
0.50
53
8.1
0.6
000
0.81
010
1.00
17:00
1000
3711
110
1776
113T
Soxtacc
110
075
1000
1181
0711
(a) de:news- deneew--MMEA
(b) es:news- parl
Summrs
See
Scewne
Soee
Dan
0.35
08755
1871
8:41
E-3
6.76
0156
1.03
8.00
8.81
8.89
0553
0.50
1051
8.75
13755
3.53
17722
1737
0700
175
1114
1030
11:1
1750
112
1396
0.31
1.33
010
0.7
0143
0001
(377
1000
180
18.8
0008
0.90
022
0.85
0.32
000
0.99
0000
0.83
002
0.50
0755
0.55
0.05
0.68
8.15
6.05
0.96
0.00
0.80
0.00
331
0.55
0.1
0.70
0.83
0850
675
01:0
1:00
0196
0.00
0390
55
0.36
076
0.30
0.81
0776
8.65
8565
0190
030
8.1
0.68
022
0381
0175
0000
0.91
030
031
053
007
0.35
000
(c) de:news-pat sarl
(d) es:news- EMEA
C.3.2 DOMAIN SHIFTS
de:news-EMEA
de:news-parl
1.0
0.8
0.6
0.4
0.2
0.0
spa:tat-EM EMEA
spa:tat-parl
1.0
0.8
0.6
0.4
0.2
0.0
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
ag
AMSP
a
aeR
Figure 5: ROC-AUC curves for our uncertainty based metrics compared to common baselines for domain shifts detection. baselines represented in dashed lines.
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Under review as a conference paper at ICLR 2022
Table 10: Detailed performance results of our OOD detectors on dialog shift against the Multi WOZ dataset as reference set.
Sieer
8.87
0.57
0,55
8:3
633
0.56
8:33
0.37
0.53
1.37
8.5
6.5
0.5
8.07
16.00
1.54
335
0.32
0.63
8.4
583
0.2
023
1.80
0.43
8.37
8.63
6.6
063
0.56
14.0
180
0.5
0.66
0.57
8:2
133
076
023
0.33
0.36
188
8.67
6.3
0.1
0.9
1634
1338
0.68
0.70
8:4
E35
082
6.3
0.47
0.43
188
8.67
0.3
0.3
0.85
0.6
144
085
083
0.57
0.38
0.6
1336
0.89
1250
81
08.0
1886
8:4
147
603
083
0337
0.39
1883
886
0338
0.53
1640
133
63
01:9
0.73
81
E63
088
0.11
0.01
1102
0.53
81
8.55
0.5
0.51
0.3
0651
1.98
07
81
68
091
0:11
0330
8.10
83
8.68
016
076
0.83
0.33
6.56
07
0.56
0.50
93
690
061
033
0190
Tan
50
0.59
380
013
633
0.66
0390
0.53
0.40
(a) dailydialog-deeeeee defauut
(b) silicone-melle
A385: 3M
50173 007
MBhCC
175
prostccs
A003 33 2278 307
8033C
ER3
11
700
prone
10:10
01
0.861
0.90
8:55
5.3
0.73
0:38
0.35
0.48
1:13
0.70
0.35
0,40
0.56
1156
1.38
09
01:63
0.11
8.13
543
1.00
0.33
1.30
8.80
3.13
8.55
0.68
0.78
0.84
0776
1.8
64
0.45
0.85
8.8
563
0.53
0.56
0.35
0.60
8.1
8.41
0.33
0.8
0.85
100
1.53
335
0.30
0.28
8.27
0.80
0855
0.35
022
0.2
044
0.1
1661
12
Opn
8.40
0.52
0.65
8:5
642
088
046
0.35
0.29
1335
8.39
048
061
0.00
160
535
0877
88
006
0136
0.55
55
016
0659
(390
1156
8.33
033
11:00
1091
115
030
815
(00)
(221
100
1.54
(c) silicone-melde
(d) silicone-dydae
Sumuats
Sunt
Sceasc
Scuee
Oen
0:31
08:31
0.73
8.36
063
063
8.13
0.55
0.78
8.53
0.51
0.36
0.33
8.68
0.75
1.08
0.35
0.35
0196
5.63
0.24
0.13
1.00
0.75
3.15
0.63
0.45
062
0.81
(16)
18
014
08'68
0171
8.36
540
6/6
010
(380
0.20
3.55
0.6
0443
0.58
0.80
1166
8:45
0.70
1130
8:42
135
099
0:33
03.87
0160
3.33
8.1
0.33
0.44
1381
1863
1.34
0.31
0.27
0.56
0.6
0.8
1600
8.6
0.57
0.67
8:43
548
033
0.6
0.39
0.50
1.53
8.00
067
041
0.90
160
1.3
11:01
3.55
0066
1350
0641
1060
1118
1.38
03.90
8:47
0000
0125
0.35
064
0228
1359
0.55
0.24
099
89
595
071
633
0340
30
635
844
010
060
4371
051
8.55
(c) silicone-swda
(f) silicone-dydada
2U00-002
A1300
150
C. 228
jusiics (2:4)
AU03-121
A278-007
A0300
BRA
41
21R
pudiiee
-1
(1:81
0.75
8:57
1:18
0.1
0:15
13.31
0.68
3.14
8.35
0.16
0.56
13.5
0171
1.48
035
08'76
11.08
8.9
644
109)
01.85
6/12
3.45
8.37
8130
0091
1330
188
077
01.0
8:44
033
0377
026
8.87
8123
0279
03.53
1275
1.55
335
0.31
8.0
8.6%
046
8.3
038
0.34
810
3.61
0444
0.85
1661
1.56
Opc
3.5
0.85
811
532
0.09
0.38
0131
0.63
3.55
8.65
044
0.68
0.81
116
8318
222
8.4
0.22
0.25
8.37
0.4
0331
035
1:51
01:01
0875
E31
(96)
035
0355
833
831
885
811
000
1333
0021
8.5
g silicone-imocca
(h) silicone-mrda
de:news-EMER
de:news-parl
1.0
0.8
0.6
0.4
0.2
0.0
Spa:tat-EMEA
spa:tat-parl
1.0
0.8
0.6
0.4
0.2
0.0
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
23
aFA*
3o.
3o:
Aog
ap:
ap

---

Under review as a conference paper at ICLR 2022
C.3.3
DIALOG SHIFTS
dailyy dialog-defaut
1.0
0.8
0.6
1.0
0.8
3.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.0
0.8
1.0
0.0
0.2
0.4
0.6
08
Rrg
Figure 7: ROC-AUC curves for our uncertainty based metrics compared to common baselines for dialog shifts detection. baselines are represented in dashed lines.
daily. dialog-defautt
dy da
iemocap
0.2
6.0
meld
mrda
swda
0.4
6.2
2M
30;
30;
30;
R0g
9pn
Figure 8: Rocauc curves for our reference based metrics compared to common baselines for dialog shift detection. baselines are represented in dashed lines.
D NTM PERFORMANCE
Surprisingly we show that common OOD detectors tend to exclude samples that are well handled by the model and keep some that are not leading to decreasing overall performance in terms of translation metrics. Moreoever it seems this phenomenon is more dominant in reference based detectors. We show that our uncertainty based detectors mostly avoir that downfall and provide good OOD detection and improved translation performances.
D. ABSOLUTE PERFORMANCES
is clear (somewhat expected) that NMT models do not perform as well on OOD data as we can in Tab. 1 lb. However, find that OOD detectors able remove most of the worst samples and keep enough well translated samples so that with correct filtering our method actually allow the model to achieve somewhat acceptable BLEU scores.
24

---

Under review as a conference paper at ICLR 2022
50-
BUEU
ALL
IN Dalaset
DGD
spa-cat
spa-por
nid-afr
Figure Gain in translation performances when filtering OOD samples with our method on different datasets and language pairs.
spa-cat
spa-por
nid-afr
spactat-parl
demewssparl
spactat-EMIs
demews--MMe
Scenario
Score
X
59.73
59.73
61.19
59.73
34.07
59.73
34.07
Ours
ap.
62.48
62.48
66.43
64.92
36.68
63.86
36.69
dyR
62.84
62.84
64.89
36.10
36.19
dg
62.66
62.66
64.53
62.66
35.87
35.87
Buselines
64.77
64.77
66.60
64.77
36.72
64.77
36.72
MMSP
58.63
58.63
59.59
58.63
33.00
63
33,00
Ours
dgt 60.47
60.43
33.82
60.43
33.82
60.47
61.73
60.47
33.78
60.47
33.78
Baselines
60.78
60.78
61.04
60.78
34.48
60.78
34.48
4M
33.85
59.87
(a) IN
spa-cat
spa-por
nid-afr spa:tat-parl
de:news-parl
spactal-EMEA
de:news-EMEA
Scenario
Score
X
15.73
15.40
23.79
33.17
28.36
54.38
$2.16
27.22
36.88
35.80
33.64
Ours
26.26
36.99
33.00
38.17
32.10
65.24
55.02
12.98
27.71
35.07
31.97
60.50
$4.48
Baselines
0g
16.62
33.59
34.18
17.36
18.97
23.91
30.81
26.46
41.51
34.64
Ours
apg
19.66
22.81
33.72
34.05
27.38
46.25
43.62
OFR
19.72
22.89
33.82
34.12
27.39
16.84
16.72
23.67
33.32
29.11
59.55
$2.19
16.55
19.30
25.15
30.75
27.22
59.15
54.71
(b) OOD
spu-cut
spa-por
nid-afr
spactat-parl
de:news-parl
spictat-EMEA
denews-EMEA
Scenario
Score
X
44.24
37.57
49.45
46.45
31.21
39.35
43.12
59.52
60.15
62.53
56.42
35.35
63.98
46.32
Ours
dyR
60.50
34.34
63.39
ag
48.75
40.58
57.89
48.77
34.19
61.48
45.78
Buselines
$4.37
48.54
61.70
$4.76
35.64
62.92 02
46.22
4MPP
42.62
ap:
49.72
48.34
57.76
56.97
30.37
59.52
36.91
49.81
48.40
57.72
$7.03
30.37
59.53
36.89
Buselines
0g
47.98
42,07
30.03
45.97
31.77
60.12
43.84
4M
48.70
49.44
52.91
49.61
30.42
37.86
(c) ALL
Table 11: Absolue translation performances in terms BLEU on the different subset (IN, OOD, ALL) of each dataset of our translation OOD performance benchmark.
25

---

Under review as a conference paper at ICLR 2022
spa-cat
por
nid-afr
spartal-pan partat-parl
de:news-parl
spa-tat-EMEA EMEA
de:news De:news-EMEA
Scenario
Score
Ours
dge
+3.11
+3.11
+3.70
+4.02
+2.03
+3.11
+2.11
+2.93
+2.93
+3,34
42.93
+1.79
+2.93
+1.79
Baselines
ag
+5.03
MMSP
-1.10
-1.10
-1.60
-1.10
-1.07
-1.10
-1.07
+0.70
+0.70
+0.55
+0.70
-0.26
40.70
-0.26
Ours
oFR
+0.74
+0.74
+0.54
+0.74
-0.29
+0.74
-0.29
Buselines
+1.05
+1.05
-0.15
+1.05
+0.40
+1.05
40.40
+0.14
+0.14
+0.17
+0.14
0.22
+0.14
0..22
IN
spa-cat
spa-por
nld-afr
spa:tat-parl
de:news-parl
spartar-
de:news-EMEA
Scenario
Score
Ours
an.
+11.49
+21.48
+12.02
+8.52
+5.28
+4.74
+2.44
+10.53
+21.59
+9
+5.00
+3.75
+5.87
+2.85
ag
-1.30
+1.89
+3.61
+2.31
Buselines
+0. 89
-0.70
+9.80
+7.06
45.82
+1.94
+2.24
RMSP
+1.63
+3.57
+0.12
-2.66
-1.90
17.87
17.52
+3.94
+7/41
+9.93
40.88
-0.98
-13.13
-8.54
Ours
+3.99
+7
+10.03
+0.95
-0.97
-13.56
-8.56
Baselines
4c
+1.11
+1.32
-0.12
+0.14
+0.75
+0.17
+0.002
+0.82
+3.90
+1.36
-2.43
-1.14
-0.22
+2.55
(b) OOD
spa-cat cat
por
nld-aft afr
spactal parl
de:news-pe parl
spactal: EMEA
de:news: EMEA
Scenario
Ours
02s
+15.26
+22 93
+10.42
+6.37
+3.13
+3.84
+2.99
50
0g
+4,51
+3.01
+8.44
+2.32
+2.98
+1.92
+2.66
Basclines
0r.
+30.13
+10.98
+12.26
+8.31
+3.36
+3.10
38
+5.06
-1.70
-2.24
-1.72
-3.40
-9.74
Ours
025
+5.48
+10.78
+3.31
+10.52
-0.85
-0.04
-6.20
51
Basclines
GFR
+3.74
+5.57
+10.83
+4.51
+0.58
+10.58
0.49
+0.56
+0.57
-0.02
+0.72
-6.23
GM
+4.46
+3.16
-0.79
+0.28
-5.26
(c) ALL
Denset
ALL
00D
AL1.
CO0
ALL
000
ALL
IN
OOD
ALL
IN
0,0
ALL
IN
OOD
ALL
IN
000
Sccerio
Saoe
Ours
43%
22R
639
309
4112
92%
31%
115:
155
15%
009
2009
2075
129
156
28%
206
37%
277
10%
7%
6226
195
1955
15%
19%
135
1952
105
195
250
13%
385
326
199
45%
14%
18%
129
1999
14%
206
576
1952
2056
900
110
877
16%
10%
1199
795
135
Baselnes 427
170
155
105
273
162
165
175
11%
2224
92
2099
M92
105
115
102
95
192
650
1155
02
Table 13: Share of the datasets removed when taking so that we keep 80% of the IN distribution.
26

---

Under review as a conference paper at ICLR 2022
D.2 GAINS
D.3 EFFECT OF A LARGER THRESHOLLD ON NMT PERFORMANCE
Table 15: Detailed impacts on NMT performance results per tasks (Domain- or Language-shifts) of the different OOD detectors with threshold defined to keep 99% of the IN data. We present results on the different part of the data: data, OOD data and the combination of both, ALL. For each report the absolute average BLEU score (Abs.), the average gains in BLEU (G.s.) compared to setting without OOD filtering (fo only) and the share of the subset removed by the detector (R.Sh.).
Language
X
Our
7.2%
34,315
27.015
14.6%
15.8%
44.0
45.6
17.615
1.3%
Bas.
47.3
44.0
45.6
603
18.4%
462
+2.3
7.6%
42.0
14.2%
43.5
7.%
60.4
1.0%
18.5
4.3%
46.3
2.5%
Our
47.0
42.0
-0.1
-011
0.9%
0.9%
40.3
40.3
26.6%
76.5%
43.9
43.9
13.8%
13.7%
60.5
60.5
4.00
+0.0
0.9%
0.9%
19.3
19.3
10.59
10.58
45.3
45.3
+1.4
+1.4
4.8%
4.8%
Bas.
47.0
-011
1.0%
41.6
18.1%
44.6
-07
9.6%
60.5
+0.0
108%
18.4
40.S
12.15
45.3
+1.4
5.9%
E NEGATIVE RESULTS
E.1 DIFFERENT AGGREGATION OF OOD METRICS
Most of our detectors are initially classification OOD detectors that we adapted for text gen- cration by averaging them over the generated sequences and using this aggregated score score for the whole sequence. We experimented with other aggregations such as the standard de- viation or the min/max along the sequence. If the standard deviation gave relatively good re- sults they were still less interesting that the naive average.
Table 14: Correlation between OOD scores and translation metrics BLEU and BERT-S
Bertscore IN
Bleu
ALL
OUT
ALL
OUT
Scrnaño
Score
Ours
+1.40
40:33
-0.33
-125
0:45
-1.37
-0.38
-0.27
-0.28
-0.37
-0138
+0.19
-0:30
0.0
-0.16
-0.21
0.00
Baselines
-0.22
-0.25
0.23
-0.90
-0.29
-0.06
0.15
0.14
Our
40.22
0.22
-1.24
-0.24
-1.37
-0.35
Baselines
0..0
0.05
E.2 NEGENTROPY
OF BAG OF DISTRIBUTIONS
We introduced in Sec. 3.3 the bag of distribu- tions way aggregate sequence of probability distribution and compare it to set of reference using information projections Sec. 3.3. natural idea would be to apply the Negentropy methods (Sec. 3.2) to these aggregated distributions.
More formally given a sequence of probability distribution So(x) = (nō (x, & we would compute its bag of distributions:
(x(
pe Vgt)
1B
And then compute novelty score:
Jp(p) = D(pl())
Further experiments have shown that this process was unable to discriminate OOD samples or improve performance translation. We suspect that the uncertainty each step is key capture the behavior of the language model and that this uncertainty information lost when averaging probability distribution along the sequence.
27

---

Under review as a conference paper at ICLR 2022
E.3 DIFFERENT REFERENCE DISTRIBUTIONS
In the no-reference scenario we used the uniform distribution as reference distribution to compare against. However, the tf-idf Yuan et al. (2021) distribution and the average distribution on the reference set. The latter effectively replacing the projection onto the reference set by the distance the average element of
obviously use other reference distributions. We tried natural options:
It is worth signaling that these methods falls into the reference scenario since we need it to compute these statistics. They would be interesting though if they could maintain performance while being less computationally expensive than the projection.
We found out that these references were not as efficient as the projection onto the reference set and did not achieve better performance than their no reference counterparts.
Language shifts FPR
Domain shifts
Dialog shifts
AUROC
F1
AUROC
FPR
F1
AUROC
FPR
F1
Ours
OD,
0.95
0.25
0.84
0.85
0.62
0.75
0.79
0.64
0.66
OFR
0.93
0.28
0.83
0.74
0.87
0.60
0.72
0.70
0.64
50
0g
0.89
0.44
0.77
0.76
0.78
0.71
0.65
0.76
0.57
Bas.
0MSP
0.87
0.44
0.75
0.78
0.77
0.71
0.66
0.72
0.21
0,,
0.78
0.79
0.50
0.72
0.89
0.65
0.65
0.95
0.62
OD;
0.88
0.34
0.71
0.86
0.50
0.70
0.86
0.52
0.59
Ours
OyR'
0.88
0.35
0.69
0.81
0.69
0.69
0.76
0.75
0.38
0.84
0.41
0.62
0.68
0.84
0.67
0.64
0.86
0.57
0.82
0.48
0.62
0.66
0.87
0.67
0.63
0.87
0.57
Opraaa
0.72
0.72
0.62
0.53
0.94
0.66
0.58
0.90
0.57
Oppar
0.67
0.81
0.61
0.52
0.94
0.66
0.56
0.92
0.57
Bas.
0M
0.92
0.26
0.73
0.78
0.59
0.40
0.84
0.55
0.56
0C
0.71
0.80
0.62
0.68
0.76
0.67
0.72
0.61
0.48
Table 16: Summary of the results including different custom reference distributions.
E.4 IMPACT OF ADDITIONAL FINETUNING ON IN DATA
Language shifts
Domain shifts
Dialog shifts
AUROC
FPR
F1
AUROC
FPR
F1
AUROC
FPR
F1
Ours
aD,,
0.94
0.26
0.80
0.86
0.65
0.70
0.79
0.70
0.67
0pR
0.92
0.24
0.78
0.72
0.88
0.67
0.72
0.70
0.65
50
0.89
0.44
0.72
0.76
0.85
0.67
0.65
0.76
0.59
Bas.
UMSP
0.87
0.44
0.73
0.78
0.83
0.67
0.66
0.72
0.56
0z
0.78
0.79
0.61
0.74
0.92
0.66
0.65
0.95
0.51
aD*
0.89
0.34
0.60
0.87
0.51
0.71
0.85
0.52
0.59
aFR*
0,86
0.37
0.61
0.65
0.93
0.64
0.77
0.77
0.58
0.84
0.41
0.61
0.68
0.84
0.69
0.64
0.86
0.55
piir
0.82
0.48
0.61
0.66
0.87
0.69
0.63
0.87
0.55
Ours
apgesa
0.72
0.72
0.62
0.53
0.94
0.66
0.58
0.94
0.57
Si
appar
0.62
0.85
0.67
0.55
0.96
0.58
0.56
0.93
0.57
Bas.
0MM
0.91
0.28
0.60
0.71
0.76
0.57
0.78
0.55
0.57
ac
0.70
0.80
0.63
0.65
0.76
0.64
0.71
0.61
0.48
Table 17: Summary of the results with additional finetuning of the models on the reference set. The results are similar to the results without finetuning expected since the models had been trained initially on similar distributions.
28


Reviews:
[{"review_id": "zqMgIG7H5JJ", "reviewer": "Reviewer_PSuw", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "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": "The paper explored OOD detection in text. Different from previous OOD work, this paper more focuses on realistic data shifts and domain change. To measure the out-of-domain score, the authors introduce renting divergence and fisher-ran distance. ", "summary_of_the_review": "I have some doubts on the methods. The experiment part should compare with more baselines on some common OOD datasets. Currently, nearly all the datasets are synthesized by authors and there is no demo in supplementary. ", "strength_and_weaknesses": "\nLack of baselines\nI think the experiment lacks an important baseline, the perplexity (PPL), since this work is based on large pretrained language model. The language model is originally designed to maximize the $p(x) = p(x_1)p(x_2)...p(x_n)$, which is a great anomaly detector itself. \n\nExperiments\n- Have you ever test your model on some other classical OOD tasks?\n\nProblem\n- Could the author give more explanation on no-referene scenario? Take mutual information, a special case of Renyi divergence as an example, I(P, U) = H(P) + H(U) because U and P are independent variables. H(U) is a constant variable for any distribution.  As the results, the entropy is used as the novelty score. \n- Did you fine-tune the language-model with in-domain data?\n\n\nPerformance\n- From the reviewers' experience on OOD in text, the performance could be much better using a simple plug-in structure. (The reviewer understood that the authors want to avoid the extra plug-in structure, just curious if better results can be achieved) \n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "v8lv60ZX5C", "reviewer": "Reviewer_BFEH", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": 4, "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": "The authors have provided an out of distribution detector, RAINPROOF and an out of distribution performance benchmark, LOFTER. RAINPROOF is an unsupervised model that is able to detect whether a reference sample is in or out of distribution when compared to the training distribution, without hampering the overall performance of the system.\n\nThey find the similarity score between two samples and apply a threshold to classify it as in or out of distribution. During inference, a soft distribution is extracted and used to compute an anomaly score, which is thresholded to perform the in or out of distribution classification.\n\nThe metrics used were AUROC and FPR and the authors report better performance on domain and dialog shifts. In language shifts, the performance is mixed, as the baselines perform better for , which is the data-driven notion normality.", "summary_of_the_review": "There are some typos in the paper. Particularly on line 10 of the abstract (ODD -> OOD) and section 3.3 has some grammatical errors and typos:\n1. In the with reference scenario -> In the reference scenario\n2. weakness of eq 3 is to imposes -> weakness of eq 3 is that it imposes\n3. under s1, our OOD detecor -> our OOD detector\n4. the last step consists in -> the last step consists of\n\nSome experimental results on the choice of similarity metric and reference distribution would help to understand the current selections.", "strength_and_weaknesses": "1. A major strength is that detecting the distribution does not hamper the performance of the model and is task independent.\n\n2. It is fully unsupervised, thus there is no need for annotated data.\n\n3. For similarity calculation, it would be good to provide results on how well the model does when the common similarity metrics like cosine and dot product is used.\n\n4. It would be good to see the performance of other reference distribution for u.\n\n5. Why is F1 score not considered along with AUROC.", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "IWzbo0Mv8Hr", "reviewer": "Reviewer_JtYo", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 4, "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": "This paper proposes an out-of-distribution (OOD) detection method for text generation based on the concept of information measure. More specifically, the author considers two measurements, the Renyi divergence, and the Fisher-Rao distance under both the no-reference and the reference scenarios. The empirical results show improvements with the proposed method in both dialogue generation and machine translation.", "summary_of_the_review": "The paper designed several out-of-distribution detection methods for text generation based on information measurements, which is a good exploration of the OOD problem in text generation. However, some method details require more theoretical justification.", "strength_and_weaknesses": "Strength:\n\n1. The author adopts the OOD into the scenarios of text generation, which is a new task that lacks exploration. \n\n2. The paper is clearly stated and easy to follow.\n\nWeaknesses:\n\n1. Some details of the proposed method lack theoretical justification. In equation (5), the author takes the average of the soft-distribution probabilities as a representation of the overall sentence distribution. This averaging step can lose a lot of sequential information of the input x. I am not convinced that the mean of the next-token prediction probabilities can represent the distribution information of the whole sentence in language modeling. In contrast, the token-level information measurements in equation (3) seem more reasonable to me. Why cannot the author define a token-level information measurement for the reference scenario, which might be consistent with the no-reference case?\n\n2. The sequential likelihood method is not compared for the with-reference scenario. With the reference, one can fine-tune the generator to obtain a better sequential likelihood estimation for text. The estimated likelihood can be also regarded as a score of occurrence for input sequences. Is there any specific reason why the author did not compare with this simple baseline?", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}]

Author rebuttal:

Decision: Reject