Question: What do the authors evaluate on the dataset?
Evidence: Among neural networks, LSTMs BIBREF0 are commonly used for language modeling. Although new architectures BIBREF1, BIBREF2 challenge this standard, LSTMs remain competitive for language modeling BIBREF3. However, despite the success of LM LSTMs, it is not clear what makes them so effective. In particular, are representations derived through language modeling able to effectively encode syntactic structures and relations? Do they encode them in a reliable and systematic way?
The typical metric used to compare LMs, perplexity, is not adapted to address these questions. Perplexity measures the probability assigned to held-out data from the corpus the LM is trained on. Because the held-out and training data are typically randomly extracted from an initial corpus, they have similar statistics, which is good from a machine learning viewpoint, but bad from the viewpoint of linguistic analysis: perplexity is mostly sensitive to the most common sentence types in the initial corpus and therefore will not reflect well the behavior of the LM in the tail of the distribution. In addition, the sentences extracted from a natural corpus confound several factors: syntax, semantics, pragmatics, etc. further complicating the interpretation of a good perplexity score.
To circumvent this limitation, recent work has focused on using probing techniques inspired by linguistic and psycholinguistics (for instance, grammaticality or acceptability judgments, or forced choice). In addition, instead of using sentences from the training corpus, studies rely more and more on automatically constructed test sentences, which enable for a removal of the bias in the original corpus and focus on particular linguistic phenomena. Here, we will use acceptability judgments operationalized by the log probability of sentences according to the LM and sets of synthetic sentences generated from template sentences to probe for a challenging linguistic structure: verb argument structure.
Verb argument structure provides languages with a way to link syntactic position in a sentence (subject, direct object, etc) with semantic roles (agent, patient, etc), in other words, to determine who is doing what. It is currently unknown whether neural LMs purely trained from surface statistics are able to capture this kind of structure, or whether additional information from another modality would be needed to provide some semantic grounding.
Verb argument structure is typically correlated to sentence position in many languages like English. But in other languages with relatively free word order, it is indicated by morphological markers. Here, we study German, where the arguments of a verb can occur in any position (when occurring within a relative clause), and is indicated by the case of the noun phrase (nominative, accusative, etc).
We setup a test of argument structure representation by presenting a trained LM with carefully constructed sets of sentences that either have the right set of arguments, or abnormal sentences where one case is missing or duplicated. We use word order permutations to control for unigram and positional statistics. If the LM is able to track argument structure irrespective of word order, it should assign lower grammaticality scores (log probabilities) to the incorrect sentences as compared to the correct ones.
Since at the level of the sentence, we study a global rather than local syntactic phenomenon, we depart from earlier work BIBREF4, BIBREF5, BIBREF6, BIBREF7 and do not compare pairs of sentences. Rather, we compare a set of valid grammatical variations of the template to a corresponding set of grammatical violations of the template. Thus, for each template, we measure the model's ability to discriminate grammatical sentences from ungrammatical ones using receiver operating characteristic curves, or ROC curves. We also compute the area under the ROC curve, or AUC. In our results, we often report the average AUC over templates as our metric.
We evaluate three LMs on our dataset, the two-layer LSTM of BIBREF8 trained on German Wikipedia text, as well as n-gram baselines using the same corpus. We ask proficient German speakers to annotate our sentences for grammaticality, providing a human comparison. Since some of these sentences are rather implausible because of the permutations, we also collect human meaningfulness scores. We find that our dataset is challenging for both LMs and humans and that LMs lag behind human performance.
We evaluate three LMs on our dataset, the two-layer LSTM of BIBREF8 trained on German Wikipedia text, as well as n-gram baselines using the same corpus.
Answer: The authors evaluate three LMs on the dataset