SlowGuess/NAACL_2021_OCR huggingface dataset:
Ian Porada<sup>1</sup>, Kaheer Suleman<sup>2</sup>, Adam Trischler<sup>2</sup>, and Jackie Chi Kit Cheung<sup>1</sup>
$^{1}$ Mila, McGill University
{ian.porada@mail, jcheung@cs}.mcgill.ca
2Microsoft Research Montréal
{kasulema, adam.trischler}@microsoft.com
Understanding natural language requires common sense, one aspect of which is the ability to discern the plausibility of events. While distributional models—most recently pre-trained, Transformer language models—have demonstrated improvements in modeling event plausibility, their performance still falls short of humans'. In this work, we show that Transformer-based plausibility models are markedly inconsistent across the conceptual classes of a lexical hierarchy, inferring that "a person breathing" is plausible while "a dentist breathing" is not, for example. We find this inconsistency persists even when models are softly injected with lexical knowledge, and we present a simple post-hoc method of forcing model consistency that improves correlation with human plausibility judgements.
Of the following events, a human reader can easily discern that (1) and (2) are semantically plausible, while (3) is nonsensical.
(1) The person breathes the air.
(2) The dentist breathes the helium.
(3) The thought breathes the car.
This ability is required for understanding natural language: specifically, modeling selectional preference—the semantic plausibility of predicate-argument structures—is known to be implicit in discriminative tasks such as coreference resolution (Hobbs, 1978; Dagan and Itai, 1990; Zhang et al., 2019b), word sense disambiguation (Resnik, 1997; McCarthy and Carroll, 2003), textual entailment (Zanzotto et al., 2006; Pantel et al., 2007), and semantic role labeling (Gildea and Jurafsky, 2002; Zapirain et al., 2013).
More broadly, modeling semantic plausibility is a necessary component of generative inferences
Is it plausible an [X] knits a [Y]?
clothing shirt
Figure 1: Elements in the matrix are the relative plausibility score for the event "an [X] knits a [Y]" as output by a RoBERTa model fine-tuned to model plausibility. [X] and [Y] correspond to the label of the row and column, respectively. Model scores are inconsistent with respect to the two events shown on the right.
A chef knits clothing.
: Very plausible!
A worker knits a shirt.
: Implausible!
such as conditional commonsense inference (Gordon et al., 2011; Zhang et al., 2017), abductive commonsense reasoning (Bhagavatula et al., 2020), and commonsense knowledge acquisition (Zhang et al., 2020a; Hwang et al., 2020).
Learning to model semantic plausibility is a difficult problem for several reasons. First, language is sparse, so most events will not be attested even in a large corpus. Second, plausibility relates to likelihood in the world, which is distinct from the likelihood of an event occurring in language. Third, plausibility reflects human intuition, and thus modeling plausibility at its extreme requires "the entire representational arsenal that people use in understanding language, ranging from social mores to naive physics" (Resnik, 1996).
A key property of plausibility is that the plausibility of an event is generally consistent across some appropriate level of abstraction. For example, events of the conceptual form "the [PERSON] breathes the [GAS]" are consistently plausible. Plausibility judgments follow this pattern because people understand that similar concept classes share similar affordances.
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1732-1743 June 6-11, 2021. ©2021 Association for Computational Linguistics
Furthermore, the change in plausibility between levels of abstraction is often consistent. Consider that as we abstract from "person breathes" to "organism breathes" to "entity breathes," plausibility consistently decreases.
In this paper, we investigate whether state-of-the-art plausibility models based on fine-tuning Transformer language models likewise exhibit these types of consistency. As we will show, inconsistency is a significant issue in existing models which results in erroneous predictions (See Figure 1 for an example).
To address this issue, we explore two methods that endow Transformer-based plausibility models with knowledge of a lexical hierarchy—our hypothesis being that these methods might correct conceptual inconsistency without over-generalizing. The first method makes no a priori assumptions as to how the model should generalize and simply provides lexical knowledge as an additional input to the model. The second explicitly enforces conceptual consistency across a lexical hierarchy by taking the plausibility of an event to be a maximum over the plausibility of all conceptual abstractions of the event.
We find that only the second proposed method sufficiently biases the model to more accurately correlate with human plausibility judgments. This finding encourages future work that forces Transformer models to make more discrete abstractions in order to better model plausibility.
We focus our analysis on simple events in English represented as subject-verb-object (s-v-o) triples, and we evaluate models by correlation with two datasets of human plausibility judgements. Our models build off of RoBERTa (Liu et al., 2019), a pre-trained Transformer masked language model. We use WordNet 3.1 (Miller, 1995) hypernymy relations as a lexical hierarchy.
Concretely, our contributions are:
- We evaluate the state of the art in modeling plausibility, both in terms of correlation with human judgements and consistency across a lexical hierarchy.
- We propose two measures of the consistency of plausibility estimates across conceptual abstractions.
- We show that injecting lexical knowledge into a plausibility model does not overcome conceptual inconsistency.
- We present a post-hoc method of generalizing plausibility estimates over a lexical hierarchy that is necessarily consistent and improves correlation with human plausibility judgements.
While plausibility is difficult to define precisely, we adopt the following useful distinctions from the literature:
- Plausibility is a matter of degree (Wilks, 1975; Resnik, 1993). We therefore evaluate models by their ability to estimate the relative plausibility of events.
- Plausibility describes non-surprisal conditioned on some context (Resnik, 1993; Gordon et al., 2011). For example, conditioned on the event "breathing," it is less surprising to learn that the agent is "a dentist" than "a thought" and thus more plausible.
- Plausibility is dictated by likelihood of occurrence in the world rather than text (Zhang et al., 2017; Wang et al., 2018). This discrepancy is due to reporting bias—the fact that people do not state the obvious (Gordon and Van Durme, 2013; Shwartz and Choi, 2020); e.g., “a person dying” is more likely to be attested than “a person breathing” (Figure 2).
Figure 2: An attested event is necessarily plausible in the world, but not all plausible events are attested. By the world we refer to some possible world under consideration—in this sense plausibility is an epistemic modality.
Wang et al. (2018) present the problem formulation that we use in this work, and they show that
Our implementation and data is available at https://github.com/ianporada/modeling_event_plausibility
static word embeddings lack the world knowledge needed for modeling plausibility.
The state of the art is to take the conditional probability of co-occurrence as estimated by a distributional model as an approximation of event plausibility (Zhang et al., 2020a). Our fine-tuned RoBERTa baseline follows this approach.
Similar in spirit to our work, He et al. (2020) extend this baseline method by creating additional training data using the Probase taxonomy (Wu et al., 2012) in order to improve conceptual generalization; specifically, for each training example they swap the event's arguments with its hypernym or hyponym, and they take this new, perturbed example to be an implausible event.
There is also recent work focusing on monotonic inferences in semantic entailment (Yanaka et al., 2019; Goodwin et al., 2020; Geiger et al., 2020). Plausibility contrasts with entailment in that plausibility is not strictly monotonic with respect to hypernymy/hyponymy relations: the plausibility of an entity is not sufficient to infer the plausibility of its hyponyms (i.e., not downward entailing: it is plausible that a person gives birth but not that a man gives birth) nor hypernyms (i.e., not upward entailing: it is plausible that a baby fits inside a shoebox but not that a person does).
Non-monotonic inferences have recently been explored in the context of defeasible reasoning (Rudinger et al., 2020): inferences that may be strengthened or weakened given additional evidence. The change in plausibility between an event and its abstraction can be formulated as a type of defeasible inference, and our findings may contribute to future work in this area.
2.1 Selectional Preference
Modeling the plausibility of single events is also studied in the context of selectional preference—the semantic preference of a predicate for taking an argument as a particular dependency relation (Evens, 1975; Resnik, 1993; Erk et al., 2010); e.g., the relative preference of the verb “breathe” for the noun “dentist” as its nominal subject.
Models of selectional preference are sometimes evaluated by correlation with human judgements (Ó Séaghdha, 2010; Zhang et al., 2019a). The primary distinction between such evaluations and those of semantic plausibility, as in our work, is that evaluations of semantic plausibility emphasize the importance of correctly modeling atypical yet
plausible events (Wang et al., 2018).
Closely related to our work are models of selectional preference that use the WordNet hierarchy to generalize co-occurrence probabilities over concepts. These include the work of Resnik (1993), related WordNet-based models (Li and Abe, 1998; Clark and Weir, 2002), and a more recent experiment by O Seaghdha and Korhonen (2012) to combine distributional models with WordNet. Notably, these methods make a discrete decision as to the right level of abstraction—if the most preferred subject of "breathe" is found to be "person," for example, then all hyponyms of "person" will be assigned the same selectional preference score.
2.2 Conceptual Abstraction
Our second proposed method can be thought of as finding the right level of abstraction at which to infer plausibility. This problem has been broadly explored by existing work.
Van Durme et al. (2009) extract abstracted commonsense knowledge from text using WordNet, obtaining inferences such as "A [PERSON] can breathe." They achieve this by first extracting factoids and then greedily taking the WordNet synset that dominates the occurrences of factoids to be the appropriate abstraction.
Gong et al. (2016) similarly abstract a verb's arguments into a set of prototypical concepts using Probase and a branch-and-bound algorithm. For a given verb and argument position, their algorithm finds a small set of concepts that has high coverage of all nouns occurring in said position.
Conceptual abstractions are captured to some extent in pre-trained language models' representations (Ravichander et al., 2020; Weir et al., 2020).
Given a vocabulary of subjects  $S$ , verbs  $\nu$ , and objects  $\mathcal{O}$ , let an event be represented by the s-v-o triple  $e \in S \times \mathcal{V} \times \mathcal{O}$ .
We take  $g$  to be a ground-truth, total ordering of events expressed by the ordering function  $g(e) > g(e')$  iff  $e$  is more plausible than  $e'$ . Our objective is to learn a model  $f: \mathcal{S} \times \mathcal{V} \times \mathcal{O} \to \mathbf{R}$  that is monotonic with respect to  $g$ , i.e.,  $g(e) > g(e') \Rightarrow f(e) > f(e')$ .
This simplification follows from previous work (Wang et al., 2018), and the plausibility score for a given triple can be considered the relative plausibility of the respective event across all contexts
and realizations.
While meaning is sensitive to small linguistic perturbations, we are interested in cases where one event is more plausible than another marginalized over context. Consider that person-breathe-air is more plausible than thought-breathe-car regardless of the choice of determiners or tense of the verb.
In practice, we would like to learn  $f$  without supervised training data, as collecting a sufficiently large dataset of human judgements is prohibitively expensive (Zhang et al., 2020b), and supervised models often learn dataset-specific correlations (Levy et al., 2015; Gururangan et al., 2018; Poliak et al., 2018; McCoy et al., 2019). Therefore, we train model  $f$  with distant supervision and evaluate by correlation with human ratings of plausibility which represent the ground-truth ordering  $g$ .
3.1 Lexical Hierarchy
We define  $\mathcal{C}$  to be the set of concepts in a lexical hierarchy, in our case synsets in WordNet, with some root concept  $c^{(1)} \in \mathcal{C}$ . The hypernym chain of concept  $c^{(h)} \in \mathcal{C}$  at depth  $h$  in the lexical hierarchy is defined to be the sequence of concepts  $\alpha(c^{(h)}) = (c^{(1)}, c^{(2)}, \ldots, c^{(h)})$  where  $\forall i, c^{(i)}$  is a direct hypernym of  $c^{(i+1)}$ . A lexical hierarchy may be an acyclic graph in which case concepts can have multiple hypernyms, and it follows that there may be multiple hypernym chains to the root. In this case, we take the hypernym chain  $\alpha(c^{(h)})$  to be the shortest such chain.
3.2 Consistency Metrics
Based on our intuition as to how we expect plausibility estimates to be consistent across abstractions in a hypernym chain, we propose two quantitative metrics of inconsistency, Concavity Delta (CCΔ) and Local Extremum Rate (LER). These metrics provide insight into the degree to which a model's estimates are inconsistent.
3.2.1 Concavity Delta
For a given event, as we traverse up the hypernym chain to higher conceptual abstractions, we expect plausibility to increase until we reach some maximally appropriate level of abstraction, and then decrease thereafter. In other words, we expect that consistent estimates will be concave across a sequence of abstractions.
For example, in the sequence of abstractions "penguin flies"  $\rightarrow$  "bird flies"  $\rightarrow$  "animal flies," plausibility first increases and then decreases. Our
intuition is that plausibility increases as we approach the most appropriate level of abstraction, then decreases beyond this level.
A concave sequence is defined to be a sequence  $(a_{1},a_{2},a_{3},\ldots)$  where  $\forall i,2a_{i} > a_{i - 1} + a_{i + 1}$
Let  $a_{i-1}, a_i$ , and  $a_{i+1}$  be the plausibility estimates for three sequential abstractions of an event. We define the divergence from concavity to be
$$
\delta = \left\{ \begin{array}{l l} \frac {1}{2} (a _ {i - 1} + a _ {i + 1}) - a _ {i} & 2 a _ {i} <   a _ {i - 1} + a _ {i + 1} \\ 0 & \text {o t h e r w i s e} \end{array} \right.
$$
We then define the Concavity Delta, CC $\Delta$ , to be the average  $\delta$  across all triples of conceptually sequential estimates. Ideally, a model's estimates should have low CC $\Delta$ . A higher CC $\Delta$  reflects the extent to which models violate our intuition.
3.2.2 Local Extremum Rate
LER simply describes how often a conceptual abstraction is a local extremum in terms of its plausibility estimate. Most often, the change in plausibility between sequential abstractions is consistently in the same direction. For example, from "bird flies"  $\rightarrow$  "animal flies"  $\rightarrow$  "organism flies," plausibility consistently decreases. The majority of abstractions will not be the most appropriate level of abstraction and therefore not a local extremum.
As in §3.2.1, we consider all triples of conceptually sequential estimates of the form  $a_{i-1}, a_i$ , and  $a_{i+1}$ . Formally, LER is the number of triples where  $a_i > \max(a_{i-1}, a_{i+1})$  or  $a_i < \min(a_{i-1}, a_{i+1})$  divided by the total number of triples.
A high LER signifies that plausibility estimates have few monotonic subsequences across abstractions. Therefore, a more consistent model should have a lower LER. There are, of course, exceptions to our intuition, and this metric is most insightful when it varies greatly between models.
The models that we consider are all of the same general form. They take as input an event and output a relative plausibility score.
4.1 RoBERTa
Our proposed models are structured on top of a RoBERTa baseline. We use RoBERTa in the standard sequence classification framework. We format an event in the raw form as `[CLS]` subject verb object [SEP]' where the s-v-o triple is tokenized
Figure 3: Left: The general formulation of CONCEPTINJECT; this model takes as input an event and the full hypernym chains of each argument. Right: CONCEPTMAX which calculates a plausibility score for each abstraction of an event using RoBERTa, and then takes the ultimate output to be the maximum of these abstractions.  $\sigma$  represents an element-wise sigmoid function.
using a byte pair encoding. These tokens are used as input to a pre-trained RoBERTa model, and a linear layer is learned during fine-tuning to project the final-layer [CLS] token representation to a single logit which is passed through a sigmoid to obtain the final output,  $f(e)$ .
We use the HuggingFace Transformers library PyTorch implementation of RoBERTa-base with 16-bit floating point precision (Wolf et al., 2020).
4.2 CONCEPTINJECT
CONCEPTINJECT is an extension of the existing state-of-the-art plausibility models. This model takes as input, in addition to an event, the hypernym chains of the synsets corresponding to each argument in the event. We propose this model to explore how injecting simple awareness of a lexical hierarchy affects estimates.
CONCEPTINJECT is similar in principle to OntoLSTM (Dasigi et al., 2017), which provides the entire hypernym chains of nouns as input to an LSTM for selectional preference, and also similar to K-BERT (Liu et al., 2020), which injects knowledge into BERT during fine-tuning by including relations as additional tokens in the input. K-BERT has demonstrated improved performance over Chinese BERT on several NLP tasks.
The model extends our vanilla RoBERTa baseline (§4.1). We add an additional token embedding
to RoBERTa for each synset  $c \in \mathcal{C}$ . We initialize the embedding of  $c$  as the average embedding of the sub-tokens of  $c$ 's lemma.3 We refer to RoBERTa's positional embedding matrix as the  $x$ -position and randomly initialize a second positional embedding matrix, the  $y$ -position.
The model input format follows that used for RoBERTa (§4.1), with the critical distinction that we also include the tokens for the hypernyms of the subject and object as additional input.
For the subject  $s$ , we first disambiguate the synset  $c$  of  $s$  using BERT-WSD (Yap et al., 2020). Then for each hypernym  $c^{(i)}$  in the hypernym chain  $\alpha(c)$ , the token of  $c^{(i)}$  is included in the model input: this token takes the same  $x$ -position as the first sub-token of  $s$  and takes its  $y$ -position to be  $i$ , the depth in the lexical hierarchy. Finally, the  $x$ -position,  $y$ -position, and token embedding are summed for each token to compute its initial representation (Figure 3).
The hypernyms of the object are included by the same procedure. Non-synset tokens have a  $y$ -position of zero. CONCEPTINJECT thus sees an event and the full hypernym chains of the arguments when computing a plausibility score.
${}^{2}$  Technically, RoBERTa's [CLS] and [SEP] tokens are  $< s >$  and  $< / s >$  .
3We refer to the name of a synset as the synset's lemma, e.g. the lemma of the synset [dog.n.01] is taken to be "dog." For synsets that correspond to multiple lemmas, we randomly sample one.
4.3 CONCEPTMAX
CONCEPTMAX is a simple post-hoc addition to the vanilla RoBERTa model (§4.1). We compute a score for all abstractions of an event  $e$  and take the final plausibility  $f(e)$  to be a soft maximum of these scores. This method is inspired by that of Resnik (1993) which takes selectional preference to be a hard maximum of some plausibility measure over concepts.
Again, we use BERT-WSD to disambiguate the synset of the subject,  $c_{s}^{(h)}$ , and the synset of the object,  $c_{o}^{(l)}$ . Using RoBERTa as in §4.1, we then compute a plausibility score for every triple of the form  $(c_{s}^{(i)}, v, c_{o}^{(j)})$  where  $c_{s}^{(i)}$  and  $c_{o}^{(j)}$  are hypernyms in the hypernym chains  $\alpha(c_{s}^{(h)})$  and  $\alpha(c_{o}^{(l)})$ , respectively. Synsets are represented by their lemma when used as input to RoBERTa. Finally, we take the LogSumExp, a soft maximum, of these scores to be the ultimate output of the model (Figure 3).
During training, we sample only three of the abstractions  $(c_s^{(i)},v,c_o^{(j)})$  to reduce time complexity. Thus we only need to compute four total scores instead of  $h\times l$ . At inference time, we calculate plausibility with a hard maximum over all triples.
4.4 Additional Baselines
RoBERTaZero-shot We use MLConjug4 to realize an s-v-o triple in natural language with the determiner "the" for both the subject and object, and the verb conjugated in the indicative, third person tense; e.g., person-breathe-air  $\longrightarrow$  "The person breathes the air." We first mask both the subject and object to compute  $P(o|v)$ , then mask just the subject to compute  $P(s|v,o)$ . Finally we calculate  $f(e) = P(s,o|v) = P(s|v,o)\cdot P(o|v)$ . In the case that a noun corresponds to multiple tokens, we mask all tokens and take the probability of the noun to be the geometric mean of its token probabilities.
GloVe+MLP The selectional preference model of Van de Cruys (2014) initialized with GloVe embeddings (Pennington et al., 2014).
n-gram A simple baseline that estimates  $P(s, o|v)$  by occurrence counts. We use a bigram model as we found trigrams to correlate less with human judgments.
$$
P (s, o | v) \approx \frac {\operatorname {C o u n t} (s , v) \cdot \operatorname {C o u n t} (v , o)}{\operatorname {C o u n t} (v) ^ {2}} \tag {1}
$$
e	e'
animal-eat-seed	animal-eat-area
passenger-ride-bus	bus-ride-bus
fan-throw-fruit	group-throw-number
woman-seek-shelter	line-seek-issue
Table 1: Training examples extracted from Wikipedia. Event  $e$  is an attested event taken to be more plausible than its random perturbation  ${e}^{\prime }$  .
Models are all trained with the same objective to discriminate plausible events from less plausible ones. Given a training set  $\mathcal{D}$  of event pairs  $(e,e^{\prime})$  where  $e$  is more plausible than  $e^{\prime}$ , we minimize the binary cross-entropy loss
$$
L = - \sum_ {(e, e ^ {\prime}) \in \mathcal {D}} \log (f (e)) + \log (1 - f \left(e ^ {\prime}\right)) \tag {2}
$$
In practice,  $\mathcal{D}$  is created without supervised labels. For each  $(e,e^{\prime})\in \mathcal{D}, e$  is an event attested in a corpus with subject  $s$ , verb  $v$ , and object  $o$ .  $e^{\prime}$  is a random perturbation of  $e$  uniformly of the form  $(s^{\prime},v,o)$ ,  $(s,v,o^{\prime})$ , or  $(s^{\prime},v,o^{\prime})$  where  $s^{\prime}$  and  $o^{\prime}$  are arguments randomly sampled from the training corpus by occurrence frequency. This is a standard pseudo-disambiguation objective. Our training procedure follows recent works that learn plausibility models with self-supervised fine-tuning (Kocijan et al., 2019; He et al., 2020; Zhang et al., 2020a).
For the models that use WordNet, we use a filtered set of synsets: we remove synsets with a depth less than 4, as these are too broad to provide useful generalizations (Van Durme et al., 2009). We also filter out synsets whose corresponding lemma did not appear in the training corpus.
The WordNet models also require sense disambiguation. We use the raw triple as input to BERTWSD (Yap et al., 2020) which outputs a probability distribution over senses. We take the argmax to be the correct sense.