Testing the Ability of Language Models to Interpret Figurative Language
Abstract
Figurative and metaphorical language are commonplace in discourse, and ﬁgurative expressions play an important role in communication and cognition.
However, ﬁgurative language has been a relatively under-studied area in NLP, and it remains an open question to what extent modern language models can interpret nonliteral phrases.
To address this question, we introduce Fig-QA, a Winograd-style nonliteral language understanding task consisting of correctly interpreting paired ﬁgurative phrases with divergent meanings.
We evaluate the performance of several state-of-the-art language models on this task, and ﬁnd that although language models achieve performance signiﬁcantly over chance, they still fall short of human performance, particularly in zero- or few-shot settings.
This suggests that further work is needed to improve the nonliteral reasoning capabilities of language models.
Introduction
All our words are but crumbs that fall down from the feast of the mind (Gibran, 1926).
When humans read such a metaphorical phrase, how do they interpret it?
Conceptual metaphors structure our everyday language and are used to map everyday physical experiences and emotions onto abstract concepts (Lakoff and Johnson, 1981).
They allow us to communicate complex ideas, to emphasize emotions, and to make humorous statements (Fussell and Moss, 2008).
However, despite relating words in a way that differs from their accepted deﬁnition, these phrases are readily interpreted by human listeners, and are common in discourse (Shutova, 2011), occurring on average every three sentences (Mio and Katz, 1996; Fussell and Moss, 2008)
The ability to interpret ﬁgurative language has been viewed as a bottleneck in natural language un- derstanding, but it has not been studied as widely as literal language (Shutova, 2011; Tong et al., 2021).
Figurative language often relies on shared commonsense or cultural knowledge, and in some cases may be difﬁcult to solve using language statistics.
This presents a challenge to language models (LMs), as strong LMs trained only on text may not be able to make sense of the physical world, nor the social or cultural knowledge that language is grounded in (Bender and Koller, 2020; Bisk et al., 2020).
Most previous work on ﬁgurative language focuses on metaphor detection, where a model is trained to identify the existence of metaphors in text (Tsvetkov et al., 2014; Stowe and Palmer, 2018; Leong et al., 2020), with datasets consisting mostly of conventionalized metaphors and idioms in wide use.
However, identifying these common metaphors that already appear often in language may be an easy task for LMs, and not fully test their ability to interpret ﬁgurative language.
The little work that exists on metaphor interpretation frames it as a task linking metaphorical phrases to literal rewordings, either through paraphrase detection (Bizzoni and Lappin, 2018) or paraphrase generation (Shutova, 2010; Su et al., 2017; Mao et al., 2018) (details in § 7) Another line of work probes for metaphorical understanding in LMs, but this is similar to the metaphor detection task, in that the LM is not actually asked to choose an interpretation for the metaphor (Pedinotti et al., 2021; Aghazadeh et al., 2022). While interesting, this work does not take into account the fact that metaphors are rich with different implications that may vary depending on the context.
In this work, we ask whether or not LMs can correctly make inferences regarding creative, relatively novel metaphors generated by humans.
This task is harder for two reasons: (1) inference is harder than identiﬁcation or paraphrasing , as it requires understanding the underlying semantics, and (2) the metaphors in our dataset are novel cre- ations, and many may not appear even once in the
LMs’ training data.
We propose a minimal task inspired by the Winograd schema (Levesque et al., 2012), where LMs are tasked with choosing the entailed phrase from two opposite metaphorical phrases.
An example of a paired sentence is "Her commitment is as sturdy as (plywood/oak)".
The correct answer would be either "She was (committed/uncommitted)".
This can also be seen as an entailment task, where input x is the premise, and the output y is the hypothesis.
We crowdsource a benchmark Fig-QA , consisting of 10,256 such metaphors and implications (§ 2), which can be used to evaluate the nonliteral reasoning abilities of LMs or for more broad studies of ﬁgurative language in general (we provide preliminary analyses in § 3).
Through extensive experiments over strong pre-trained LMs (§ 4), we ﬁnd that although they can be ﬁne-tuned to do reasonably well, their few-shot performance falls signiﬁcantly short of human performance (§ 5).
An in-depth analysis (§ 6) uncovers several insights: (1) LMs do not make use of the metaphorical context well, instead relying on the predicted probability of interpretations alone, (2) the task of associating a metaphor with an interpretation is more difﬁcult than the reverse, (3) even strong models such as GPT-3 make inexplicable errors that are not well-aligned with human ones, indicating that further work is needed to properly model nonliteral language.
Dataset Creation and Validation 2. Crowdsourcing Task
We crowdsourced data from workers on Amazon
Mechanical Turk ( details in Appendix A).
Workers were asked to generate paired metaphors with different meanings, as well as literal implications of the two metaphors in context.
We instructed workers to try to generate rare or creative metaphors, namely “metaphors that would not appear often in text on the internet, books, social media, or news sites, but that can still be easily understood by people.”
Workers were given examples of valid pairs that ﬁt the format, and examples of invalid ones to discourage errors.
Some examples of generated pairs are displayed in Table 1.
In order to help workers, we employ the randomness as genesis and narrow limits of change principles of Cognitive Load Theory (Sweller, 2006).
To add soft constraints, we generate 3 different random words to be shown to each batch of workers.
However, workers were not required to use these words, as we wanted to encourage maximal diversity.
In order to ensure that the random words were metaphorically rich, we selected them based on metaphorical frames in Lakoff and Johnson (1981).
2. Data Validation
The dataset was manually validated by three authors of this paper.
Each author covered roughly one-third, evenly split between training, validation, and test.
Examples were excluded if they (a) did not make sense given the ﬁgurative expression, (b) had grammar or spelling errors that rendered them unintelligible, or (c) did not follow the format of the task.
Examples of excluded samples are included in Appendix B. We collected 13,324 sentences and interpretations from the crowdsourcing task, and 10,256 sentences remained after ﬁltering.
2. Final Dataset
The release version of our dataset contains the named data splits in Table 2.
The medium train, dev, and test splits were generated from partitioning the ﬁrst stage of data collected.
The large train split additionally contains all the new examples from the second collection stage, and the small train split is a small random sample.
Figurative Language Typologies
In this sample, we perform an analysis of the collected data to demonstrate its trends and categorize examples for further error analysis.
Speciﬁcally, we examine (a) subjects, objects, and relations, and (b) types of common-sense knowledge needed to interpret the metaphor.
3. Figurative Language Structure
We note that most metaphors and similes can be characterized by three components, ( S, R, O ) , where S is a subject, R is a relation, and O is an object.
For instance, "Her commitment is as sturdy as plywood" can be written (Her commitment, sturdy, plywood).
Interpretation involves inferring an attribute of the subject by extracting a relational attribute from the object (Fauconnier and Turner, 2003).
In a simile, R is explicit, whereas it is usually implicit in a metaphor.
The most common [Table] [Table] subjects, relations, and objects in the medium train dataset are shown in Figure 1.
These were obtained by ﬁrst segmenting the phrases with syntactic patterns constructed from observation, followed by lemmatization and removal of punctuation and determiners "the", "an", "a" and "that".
There areunique subjects, 646 unique relations, and 1, unique objects in the medium training set.
3. Common-sense Knowledge Types
Next, we examined the test set to determine the types of commonsense knowledge needed to interpret metaphors.
Through thematic analysis, we devised 4 categories based on commonsense knowledge, which are not mutually exclusive: common-sense object knowledge, visual metaphors, common-sense social understanding, and cultural knowledge.
The same 3 paper authors annotated the test set for these categories, with annotators responsible for separate categories.
Common-sense object knowledge consisted of metaphors that made reference to properties of common objects and animals, such as volume, height or mass of objects, or properties of materials.
68.35% of the test-set was found to require common-sense object knowledge.
Visual metaphors were a subset of commonsense object metaphors, primarily relying on the visual modality, including attributes such as brightness or colour.
Some visual metaphors also sketched a vivid visual scene.
These examples comprised 14.73% of the test set.
Common-sense social understanding examples required knowing about how humans would react in different circumstances, or required knowing about human emotions.
These examples comprised 27.55% of the test set.
Cultural metaphors required knowing cultural traditions, works of art/artefacts, or religion.
Due to crowdworkers being recruited from the US, these are centered around US culture.
These examples comprised 16.56% of the test set.
Baseline Models and Evaluation 4. Auto-regressive Language Models
Auto-regressive LMs generate a probability distribution of the next token given all preceding tokens.
[Table]
Common
[Table]
The new mattress is just as comfortable as sleeping on a (cloud/rocks outside)
Visual
The professor’s argument had the clarity of a (crystal glass/marine fog)
Common-sense (social)
She is as embarrassed as a kid that (forgot homework/got an A)
Cultural
The construction was as disastrous as the (1981 musical Cats/The 2019 ﬁlm based on the musical Cats)
As such, we can directly compute the probability of a sentence by multiplying the conditional probability of each token at every time step.
[Equation]
The ability to directly extract probabilities enables the zero-shot reasoning of these LMs.
For a pair of metaphorical expressions x 1 and x 2 with two corresponding interpretations y 1 and y 2 , we feed in the concatenation of the metaphor and the interpretation to the pretrained model without ﬁnetuning.
We deﬁne “forward” and “backward” probabilities assigned to interpretations and ﬁgurative language expressions, respectively.
For the forward probability , for ﬁgurative phrase x i and correct answer y i , we take
[Equation]
since there are only two answer options.
From this, we can calculate accuracy when we taking the indicator of P ( y i | x i ) > 0 .
Similarly for the backward probability (predicting phrase based on answer), we take
[Equation]
with analogous backward accuracy.
We examine three state-of-the-art large transformer-based LMs of this category: GPT- (with 117M parameters, trained on 40GB of text), GPT-neo (with 1.3B parameters, trained on 800GB of text) and GPT-3 (4 variants between 350M and 175B parameters, trained on 45TB on text)
(Radford et al., 2019; Black et al., ; Brown et al., 2020).
We also examine the performance of these models after ﬁnetuning on the training data.
GPT-2 and GPT-neo were trained with a batch size of 8, with early stopping on the medium dataset with a patience of 1 epoch, and a minimal hyperparameter search was done with learning rates 1e-5 to 5e-5.
GPT-3 was trained with the default parameters of the GPT-3 ﬁnetuning API.
4. Masked Language Models
We also evaluate the performance of masked LMs on this task.
Unlike auto-regressive LMs, masked
LMs cannot directly output the probability of a sentence, so it is not possible to directly test the zero-shot performance of these models.
Instead, we test the transfer performance by ﬁrst ﬁnetuning them in two ways: on WinoGrande, which is also a binary choice task based on commonsense reasoning, and on several NLI datasets, including SNLI, MNLI, FEVER-NLI and ANLI (Nie et al., 2020; Sakaguchi et al., 2020).
The input to the model trained on WINOGRANDE is formatted as [CLS][metaphor][SEP] [answer1][SEP][answer2] , and we use an extra linear layer on the [CLS] token embedding to perform the classiﬁcation.
In addition to the transfer performance, we also use contrastive ﬁnetuning by feeding in each metaphor along with both answer choices, and training the model with our dataset to classify which answer is correct.
For the
NLI model, we examine accuracy using all three labels the model was originally trained with (entailment, neutral, and contradiction), as well as using a forced binary choice paradigm in which the logits for the contradiction label are subtracted from the logits for the entailment label, and the higher "entailment score" is the ending the model predicts.
We treat these two conditions as the analog of “zero-shot" for these models.
We examine two masked LMs that are commonly used as baselines on many NLP tasks:
BERT (Devlin et al., 2019), a transformer-based
LM jointly trained on the masked LM and next sen- tence prediction objectives, and RoBERTa (Liu et al., 2019), an improved variant of BERT which consistently outperforms BERT across most tasks.
We use the large variant of both models (350M parameters).
BERT and RoBERTa were ﬁnetuned on the medium dataset for 8 epochs with batch size 8, following the setting in (Sakaguchi et al., 2020).
A hyperparameter search was done with learning rates 5e-6 to 2e-5.
Both BERT and RoBERTa were used for the Winogrande experiments, while only
RoBERTa was used for the NLI experiment.
4. Forced-choice Paradigm
Due to the inherent creativity of metaphors, there may be different interpretations of the same metaphor.
For instance, in Table 1, the example "he hustles like he’s a billionaire’s son" could also be interpreted in other ways, for instance "he uses his father’s contacts and social privileges to make money".
In a structural-mapping context, the forced choice between two answers constrains the possible meaning of the metaphor to be along one axis (Gentnder and Bowdle, 2008).
In this case, it would be whether or not he is required to work hard.
Of course, many of these metaphors have other valid interpretations.
In the "billionaire’s son" example, another valid axis of interpretation could be the manner in which he works.
For instance, the alternative pair could be "he hustles like he’s a (billionaire’s son | single mother working three jobs)" with answers "he (uses his contacts and social privileges to make money | works extremely long hours with multiple ventures to make money)".
It is possible that LMs could come up with other valid interpretations that are not the ones originally intended, motivating us to also look at generation performance in section § 5.2.
To estimate the expected human performance on this task, we ran a benchmark on the test set withhuman volunteers who were not authors of the paper.
The human annotators were not shown any training examples, so this would be equivalent to the zero-shot setting for models.
Participants ranged from 20 to 29 years old, and there weremale and 5 female participants.
5 each were nativeand non-native English speakers respectively.
Participants were mainly graduate student volunteers.
We shufﬂed the test set and split it into 10 partitions of ≈ 115 examples for each annotator. The [Table]
examples were presented with pairs shufﬂed and separated, in order to create a better comparison with model performance.
Due to differences in vocabulary or cultural background, we instructed participants to mark examples where they weren’t conﬁdent, such as those that contained words or cultural references they didn’t understand.
Results 5. Inference Results
The ﬁrst question is whether strong LMs can interpret metaphors at all when presented with two opposing meanings, in zero-shot or supervised settings .
These results are presented in Table 4.
The results for masked language models are higher than those for autoregressive language models, and ﬁne-tuning signiﬁcantly improves performance for all models.
Zero-shot Performance
For the zero-shot setting, we examine the test accuracy based on zeroshot forward probabilities for the GPT models, and the pseudo "zero-shot" transfer performance for
BERT and RoBERTa using models pretrained on the WinoGrande task (Sakaguchi et al., 2020).
As shown, the GPT-3 models outperform the GPTand GPT-neo models.
Among the GPT-3 models, there is a clear correlation between model size and performance, with the largest model (GPTDavinci) achieving the highest zero-shot test accuracy.
BERT and RoBERTa achieve accuracies within the range of GPT-3 models.
While our models mostly perform much better than chance in the zero-shot setting, there is still a large gap of 26 percentage points between our best model and human level performance.
Fine-tuned Performance
For the ﬁne-tuned setting, all listed models are ﬁne-tuned on the small dataset split.
GPT models were trained with language modeling loss, whereas BERT and RoBERTa are trained with contrastive loss.
We did not evaluate ﬁne-tuning of GPT-3 Davinci due to budget.
Overall, ﬁne-tuning improved accuracy signiﬁcantly for all models, with GPT-3 models uniformly improving by about 13 percentage points, and BERT/RoBERTa improving by about 25 points.
Our best model after ﬁne-tuning is RoBERTa, which reaches within 5% of our human performance.
Prompting
We also experiment with prompting methods.
Firstly, we use a simple sufﬁx prompting method, where we simply append the phrase "that is to say" between the metaphor and the interpretation, which we hypothesized may "explain" to the LM that the previous statement is ﬁgurative.
We also evaluate the effectiveness of the examples method, by appending k random correct metaphor/interpretation pairs before the actual pair we are testing.
The results of these experiments can be seen in Figure 2.
We found that the sufﬁx method provided a small (1-2%) improvement over the baseline, while the example method was generally ineffective.
Backward accuracies
Note the accuracies reported in this section are for the forward direction, and the backward direction is reported in Appendix C. Backward accuracies are lower, with
GPT-3 Curie for example having a 7% reduction in accuracy in the zero-shot case.
This suggests that selecting a metaphorical expression to match a literal phrase is more challenging than the reverse for LMs. Paired Evaluation
Because our dataset is formatted as a Winograd schema, we can take advantage of group scoring to evaluate models more stringently (Elazar et al., 2021). We found that performance for autoregressive models plummeted under this evaluation scheme, while masked language models also suffered in accuracy. The human scores were least affected. Details are in Appendix D.
This is most likely related to the phenomenon found in § 6.1.
GPT-2 GPT-neo 1.3B GPT-3 Ada 0.
A cc u r a cy
Zero-shot Prompting (k = 3)
Prompting (k = 5) Prompting (k = 10)
Prompting (suffix)
5. Generation Results
Next, we examine if models can generate sensible interpretations for metaphors .
Given the difﬁculty of evaluating text generation, compounded by the difﬁculty of ﬁgurative language, we opted for manual evaluation of one tenth of the test dataset using generations of the strongest autoregressive model: GPT-3 Davinci ( ≈ 175B parameters).
The metaphor was given as input to the model, and 4 completions were generated for each metaphor, with a maximum length of 100 tokens.
Completions were also truncated to the ﬁrst sentence, as initial experiments showed that contradictory statements (e.g. "he was talented.
But he was not very talented") were often generated across subsequent sentences.
Sufﬁx prompting was also used because of the lack of context, with "That is to say, " appended to each metaphor.
Only the ﬁrst sentence of the output was evaluated.
The temperature parameter was determined through grid search through values [0.2, 0.4, 0.6, 0.8, 1] on a small separate set of metaphors.
A human annotator inspected the generated completions and found that a temperature of 0.4 produced the most correct results.
Three paper authors labelled completions generated by GPT-3 Davinci as either correct, incorrect, or literal.
In some cases, there were valid interpretations that were not the same as the answer given by crowdworkers, which were also marked correct.
If the model simply restated the metaphor with no interpretation, the completion was marked as literal.
Because some metaphors are ambiguous when presented without context, those examples were not counted.
The inter-rater reliability was moderate due to differing standards for correctness (Krippendorff’s α = 0.5567).
The majority vote was taken between annotators’ judgments.
GPT-3 Davinci’s accuracy, counting literalized metaphors as incorrect, was 50.8%.
Not counting literalized metaphors, accuracy was 63.9%.
In 37.7% of cases, GPT-3 generated contradictory completions among the 4 completions.
There was at least one correct completion for 78.1% of the metaphors, but only 19.3% of metaphors had all completions correct.
Examples of annotated generations can be found in Appendix G.
Performance and Error Analysis
With these results in mind, we examine what kinds of errors models make, and what factors make the task difﬁcult. .
This is covered in § 6.
We ﬁnd that autoregressive models rely on the predicted probability of each answer by itself to predict the answer, and that this holds true for all models, before and after training.
We ﬁnd that models have difﬁculty in interpreting "sarcastic" metaphors, and sometimes inexplicably interpret very simple metaphors wrong.
We also examine error typology according to the commonsense typology of § 3.2 and ﬁnd that models improve significantly on object, visual and social commonsense when trained, but not on cultural commonsense.
6. Reliance on Probability of Answers
We ﬁnd that models often rely solely on the predicted probability of answers y 1 and y 2 to make their ﬁnal predictions, regardless of the context.
This led models to make the same prediction for the paired sentences in many cases.
Figure 3 and Table 5 show that this trend improves with ﬁnetuning, and that GPT-3 is best able to disentangle the probability of y i and the probability of P ( y i | x i ) , but all three models show a heavy tendency to predict based on the relative probability of an answer alone.
We hypothesize that this may be one reason why
BERT and RoBERTa achieve the best ﬁnetuned performance; they use a contrastive ﬁnetuning strategy providing both the correct and incorrect options as input to the model.
On the other hand, the GPT models were ﬁnetuned with only the correct option, making the comparison unfair.
One way to ﬁnetune GPT models contrastively is to include both options into a cleverly engineered prompt, but we [Table] leave this as a direction for future work.
6. Other Factors Inﬂuencing Correctness
We also examined the inﬂuence of several other factors on correctness.
The point-biserial correlation between length of the context phrase and the binary correctness value was -0.1544 with a p-value of 1 .
50 × 10 − 7 , indicating that longer phrases are harder to interpret correctly.
The point-biserial correlation between answer probability and binary correctness was 0.1840, with a p-value of 3 .
50 × 10 − 10 , indicating that examples where the answer was already more probable were more likely to be an- swered correctly, in line with our ﬁndings that models tended to predict the answer that was already more plausible alone.
Furthermore [Table] we conducted an analysis on subjects, objects, and relations as deﬁned in § 3.1.
We examined accuracy by part of speech patterns in each part of the metaphor, as well as by wordnet hypernyms present in each part of the metaphor.
This is detailed in Appendix E and Appendix F (Fellbaum, 1998).
We used NLTK for POS tagging (Loper and Bird, 2002).
6. Qualitative Analysis of Error Trends Common Sense Knowledge
We ﬁrst examine the error tendencies by the type of common sense knowledge described in § 3.2.
Table 6 summarizes accuracies for these types of commonsense questions compared to humans
[Table]
We ﬁnd that both humans and trained models tend to ﬁnd object commonsense and visual commonsense metaphors easier to interpret.
We ﬁnd that as models improve, most of the performance gain comes from the object, visual and social commonsense categories.
Interestingly, the untrained models do quite well on cultural examples, but do not improve much on the culture category when trained.
This makes sense, as the cultural examples tend to be quite disparate, so training would not help as much with other examples.
Sarcastic Metaphors
For both humans and LMs, many of the errors are "sarcastic" metaphors, such as saying "the girl was as bubbly as still water" to mean "the girl was bland", rather than "the girl was vivacious".
These sentences can be difﬁcult if the model or human focuses on simple word association (bubbly with vivacious) without reading the entire sentence to understand the sarcasm.
Inexplicable Errors
We examined the errors made by GPT-3 Curie (trained) and found that there was little overlap with mistakes made by humans.
Of the 64 human errors, 13 were also errors made by GPT-3.
GPT-3 made many more "obvious" errors, such as predicting "The ball is a big red sun" to mean "the ball is small" rather than "the ball is big and red" This is in contrast to the sentences in which humans made errors, which often contained rare vocabulary or unfamiliar cultural references.
Related work 7. Figurative Language Identiﬁcation
Most existing work focuses on identifying ﬁgurative language at the word level.
The VU Amsterdam Metaphor Corpus (VUA) is the largest available corpus of metaphorical language, annotated by humans (Steen et al., 2010).
Two shared tasks on metaphor identiﬁcation have been run (Leong et al., 2018, 2020).
Both have utilized the VUA corpus, and the latter also introduced the TOEFL corpus, sampled from essays written by non-native English speakers (Leong et al., 2020; Beigman Klebanov et al., 2018).
Most participants in the shared tasks used neural models, notably BERT, RoBERTa, and
Bi-LSTMs (Leong et al., 2020; Bizzoni and Ghanimifard, 2018; Gao et al., 2018; Pramanick et al., 2018).
These models are generally improved when augmented with semantic data, such as concreteness, and multimodal information.
Another line of work focuses on probing models to determine the extent of metaphor recognition.
For instance, BERT assigns higher pseudolog-likelihood scores to metaphors than nonsense expressions, and its contextualized representations show some signs of contextualizing the object domain (Pedinotti et al., 2021).
Another study uses linear probes trained on layers of BERT to predict whether a word usage is literal or nonliteral, and ﬁnds that this can be done effectively, especially using middle layers as a representation (Aghazadeh et al., 2022),
Despite the utility of these tasks and datasets, they have drawbacks.
Most of the metaphor use is conventional, so this task does not capture novel metaphors well.
The word-level annotation also does not lend itself well to capturing extended con- ceptual metaphors.
Finally, metaphor interpretation may be a more difﬁcult, although less studied, task.
7. Figurative Language Interpretation
Recent studies mostly focus on metaphor paraphrases, either through identiﬁcation (Bizzoni and
Lappin, 2018) or generation (Shutova, 2010; Su et al., 2017; Mao et al., 2018).
However, there has not been as much work done on interpretation as on detection, and framing metaphor interpretation as a paraphrase task may not capture the emergent meaning of metaphors, such as the intended emotion, or the interaction of subject, relation and object in the metaphor (Tong et al., 2021; Mohammad et al., 2016).
Other work has focused on interpreting ﬁgurative language in narratives in context, based on plausible continuations of ﬁgurative language such as idioms and similes from stories (Chakrabarty et al., 2021a) or dialogues (Jhamtani et al., 2021).
This represents a promising direction, and our work focuses on expanding our understanding of LMs’ ability to interpret non-conventionalized metaphors.
7. Other Figurative Language Datasets
We note that there are several other challenging
NLI datasets available which contain ﬁgurative language, including the DNC corpus, and the RTE dataset (Poliak et al., 2018; Chakrabarty et al., 2021b).
Other datasets, such as RiddleSense, explicitly test models through difﬁcult commonsense inference, involving ﬁgurative language (Lin et al., 2021).
Our work is distinguished by the Winograd schema format, as this format provides a better guard against the possibility that models have simply memorized common word associations that occur in ﬁgurative language.
Additionally, we specifically instructed crowdworkers to be creative, and this resulted in longer ﬁgurative phrases which require more detailed commonsense knowledge.
It is likely that a fair number of these ﬁgurative phrases have never appeared in any training corpus.
However, our ﬁgurative phrases also differ from riddles, as they are not supposed to be difﬁcult to reason about, given that the source, relation and object are properly contextualized.
7. Human Language Processing
Humans typically do not have any more difﬁculty processing metaphorical statements in context compared to literal statements (Fussell and Moss, 2008;
Glucksberg, 2003).
This may be because certain words serve as a dual reference , which is to say they refer simultaneously to a physical referent and an abstract superordinate category (Glucksberg, 2003).
For instance, "shark" may refer to literal sharks, as well as anything that is considered vicious, leading to utterances such as "that lawyer is a shark".
Metaphorical language processing has also been studied in second-language learners, in the case of idioms.
In most cases, the meaning of an unfamiliar idiom is inferred from the context or from word association (Cooper, 1999; Carston and Wearing, 2011; Wolff and Gentner, 2000).
As LMs excel at word-association based tasks, this is an encouraging ﬁnding.
However, there is still a gap between LM and human performance even in our task, in which one answer is obviously wrong when the input is correctly understood.
We take into account that these results are for conventionalized ﬁgurative language and that some of the more creative phrases in this dataset may take a longer time to process for humans as well.
This is especially true for non-native English speakers.
However, the high human accuracy on this task with half the participants being non-native English speakers suggests that this was not a major barrier.
Conclusion
We present a Winograd-like benchmark task to test the ability of LMs to reason about ﬁgurative language, based on large-scale collection of creative metaphors written by humans.
We ﬁnd a large gap between LM zero-shot and human performance on this dataset, but show that models can be ﬁne-tuned to perform well on this particular task.
We hope that this work will encourage further study of nonliteral reasoning in LMs, especially in few-shot settings.
Given that metaphorical reasoning may play a role in problem-solving and linguistic creativity, the development of models, training methods, or datasets that enable metaphorical reasoning may improve models’ abilities to reason creatively and draw analogies between situations that may appear to be different on the surface.
One avenue we hope to investigate is multimodal metaphors, as this dataset currently includes only text-based metaphors.
Nonliteral expressions also remain understudied cross-linguistically, but further work on identifying and interpreting metaphors in other languages may also improve the abilities of multilingual models.
Ethical Considerations 9. Potential Risks
Figurative language has the potential to be used in a harmful way, especially against minority and historically disadvantaged groups.
Such language is often emotionally charged or used to insult others, so we took care to remove any examples that were potentially offensive, especially toward protected groups.
We acknowledge that this was based on our own judgment, and generically insulting language (for instance, a metaphor that implies that someone is ugly) was not removed because it was not insulting toward any particular individual.
All examples from Fig-QA are also in English, as it is the language that all authors speak, and this was a preliminary dataset, being the ﬁrst of its type that the authors have worked on.
However, ﬁgurative language is not just important in English, and we leave investigation of ﬁgurative language in other languages as future work.
9. Terms of Use of Artefacts Used
Additional datasets we used were the Winogrande dataset, SNLI, MNLI, FEVER-NLI and ANLI.
Winogrande is licensed under the Apache 2.0 license, which allows modiﬁcation and distribution, ﬁtting our use case.
SNLI is licensed under a Creative Commons Attribution ShareAlike 4.0 International license, which allows us to share and adapt the work as long as we give attribution.
The majority of MNLI is licensed under OANC, which allows free use.
The ﬁction section of this dataset consists mostly of works in the public domain, but several stories are licensed: Seven Swords is available under a Creative Commons Share-Alike [-3.
-]3.
Unported License, while Living History and Password Incorrect are available under Creative Commons Attribution 3.0 Unported Licenses.
These licenses allow sharing and adaptation with attribution.
FEVER-NLI is licensed under an MIT license, which also allows modiﬁcation, distribution, and reuse.
ANLI is licensed under Creative
Commons Attribution-NonCommercial 4.0 International, which also allows sharing and reuse as long as we give attribution.
Models used were GPT-2, GPT-neo, GPT-3,
BERT and RoBERTa.
GPT-2 and GPT-neo are licensed under an MIT license, which does not place any restrictions on its use.
BERT is licensed under an Apache License 2.0, which allows modiﬁcation and distribution.
RoBERTa is licensed under a GNU General Public License v2.0.
This ﬁts our use case as we are only running and studying the model.
GPT-3 is licensed by Microsoft, and we used the public API to receive output.
9. Computational Infrastructure and Computing Budget
To run our computational experiments, we had access to a compute cluster, but minimal compute is needed to run the experiments in this paper.
We generally did not use more than 2 GPUs at a time.
The only models that required GPU parallelism were the GPT-neo models.
An estimated 20 GPU hours are required.
Our computing budget was roughly 100 USD.
We also used roughly 20 USD on credits for the GPT-3 API. Acknowledgements
We thank Pengfei Liu, Lyuyang Hu, and Chih-Hao
Wang for helping us set up the leaderboard for this dataset on Explainaboard.
We also thank Pengfei
Liu for helping run GPT-3, Danish Pruthi for guidance on setting up the MTurk task, and all participants who contributed to the human benchmark. Lastly, we thank all the Turkers who contributed metaphors to the dataset. This work was supported in part by a CMU Presidential Fellowship and National Science Foundation Award No. 1761548. A Crowdsourcing Details
We crowdsource metaphorical expressions and their interpretations through Amazon Mechanical
Turk.
Workers were recruited from the United
States and were limited to those who had a > 98% approval rating on the platform, and who had also completed more than 1000 Human Intelligence
Tasks (HITs).
Data collection was split into two stages: in the ﬁrst stage, 1458 train examples, and all the dev and test examples were collected.
In the second stage, the remaining 6558 training examples were collected.
We identiﬁed some workers who created especially good examples in the ﬁrst stage, and recruited them back for more examples in the second stage.
Workers were paid $0.33 for each pair of sentences and were asked to generate 3 pairs at a time.
An author of this paper wrote an initial pilot set of sentences, and timed themselves while writing some sentences.
They found that each pair took around 1 minute to write, though this varied (less creative examples took less time, while more creative examples took more time).
This extrapolates to an hourly rate of 19 .
80 USD, which is above the minimum wage in all US states, where workers were located.
Our HIT task was structured as follows:
At the top of the page, the workers are shown the following instructions: "Your task is to generate three pairs of sentences with opposite or very different meanings, both of which contain rare/creative metaphors, which means metaphors that would not appear often in text on the internet, books, social media or news sites, but that can still be easily understood by people.
For each metaphor, you should also provide a literal (non-metaphorical) sentence with the same meaning."
Then, we display one example of a valid sentence pair.
There is a button that opens a modal with more detailed instructions and some more valid/invalid examples for reference.
Below that, we display three random words, which workers are encouraged to use in their sentences if they get stuck.
Finally, we display three sets of 5 text ﬁelds for workers to ﬁll in: one for the start phrase, two for each metaphorical phrase, and two for each literal interpretation.
As the user types in each start phrase, we prepend a copy of their phrase before the corresponding metaphor ﬁelds in the UI using some embedded JavaScript, which we found helped reduce confusion and resulted in less improperly formatted responses.
We launched many batches of these HITs until we had collected the desired quantity of data.
Then, we converted the form responses into sentence pairs and validated each pair by hand before adding it to our dataset.
B Invalid Examples
Figurative language examples collected from crowdworkers were excluded if they (a) did not make sense given the meaning and the metaphorical expression, (b) had grammar or spelling errors that rendered them unintelligible, or (c) did not follow the format speciﬁed by the task template.
Examples are given below:
Do not make sense given the meaning and the metaphorical expression
[Table]
2. Signiﬁcant grammar or spelling errors
[Table]
3. Do not follow
[Table]
Efforts were made to ensure that the ﬁnal dataset contains no offensive content or personally identiﬁable information.
WorkerID and other potentailly personally identifying information were not included [Table]
C Backward accuracies
[Table]
D Paired accuracies
[Table]
E Accuracy breakdown by Part-of-Speech E. Subject
[Table]
E. Relation
[Table]
E. Object
[Table]
F Accuracy breakdown by hypernyms F. Subject
[Table]
[Table]
G Generation examples
Generation examples can be found in Table
[Table]
[Table]