Working graph decoding.

combo/data/dataset.py

@@ -54,6 +54,8 @@ class UniversalDependenciesDatasetReader(allen_data.DatasetReader):
         field_parsers = parser.DEFAULT_FIELD_PARSERS
         # Do not make it nullable
         field_parsers.pop("xpostag", None)
+        # Ignore parsing misc
+        field_parsers.pop("misc", None)
         if self.use_sem:
             fields = list(fields)
             fields.append("semrel")

combo/predict.py

@@ -3,6 +3,7 @@ import os
 from typing import List, Union, Tuple
 
 import conllu
+import numpy as np
 from allennlp import data as allen_data, common, models
 from allennlp.common import util
 from allennlp.data import tokenizers
@@ -195,10 +196,11 @@ class SemanticMultitaskPredictor(predictor.Predictor):
 
         if "enhanced_head" in predictions and predictions["enhanced_head"]:
             import combo.utils.graph as graph
-            tree = graph.sdp_to_dag_deps(arc_scores=predictions["enhanced_head"],
-                                         rel_scores=predictions["enhanced_deprel"],
+            tree = graph.sdp_to_dag_deps(arc_scores=np.array(predictions["enhanced_head"]),
+                                         rel_scores=np.array(predictions["enhanced_deprel"]),
                                          tree=tree,
-                                         root_label="ROOT")
+                                         root_label="ROOT",
+                                         vocab_index=self.vocab.get_index_to_token_vocabulary("deprel_labels"))
 
         return tree, predictions["sentence_embedding"]
 

combo/utils/graph.py

@@ -3,7 +3,7 @@ import numpy as np
 from conllu import TokenList
 
 
-def sdp_to_dag_deps(arc_scores, rel_scores, tree: TokenList, root_label):
+def sdp_to_dag_deps(arc_scores, rel_scores, tree: TokenList, root_label, vocab_index=None):
     # adding ROOT
     tree_tokens = tree.tokens
     tree_heads = [0] + [t["head"] for t in tree_tokens]
@@ -12,7 +12,7 @@ def sdp_to_dag_deps(arc_scores, rel_scores, tree: TokenList, root_label):
     for i, (t, g) in enumerate(zip(tree_heads, graph)):
         if not i:
             continue
-        rels = [x[1] for x in g]
+        rels = [vocab_index.get(x[1], "ROOT") if vocab_index else x[1] for x in g]
         heads = [x[0] for x in g]
         head = tree_tokens[i - 1]["head"]
         index = heads.index(head)
@@ -30,9 +30,9 @@ def sdp_to_dag_deps(arc_scores, rel_scores, tree: TokenList, root_label):
 
 def adjust_root_score_then_add_secondary_arcs(arc_scores, rel_labels, tree_heads, root_idx):
     if len(arc_scores) != tree_heads:
-        arc_scores = arc_scores[:len(tree_heads), :len(tree_heads)]
-        rel_labels = rel_labels[:len(tree_heads), :len(tree_heads)]
-    parse_preds = arc_scores > 0
+        arc_scores = arc_scores[:len(tree_heads)][:len(tree_heads)]
+        rel_labels = rel_labels[:len(tree_heads)][:len(tree_heads)]
+    parse_preds = np.array(arc_scores) > 0
     parse_preds[:, 0] = False  # set heads to False
     # rel_labels[:, :, root_idx] = -float('inf')
     return add_secondary_arcs(arc_scores, rel_labels, tree_heads, root_idx, parse_preds)
@@ -42,7 +42,7 @@ def add_secondary_arcs(arc_scores, rel_labels, tree_heads, root_label, parse_pre
     if not isinstance(tree_heads, np.ndarray):
         tree_heads = np.array(tree_heads)
     dh = np.argwhere(parse_preds)
-    sdh = sorted([(arc_scores[x[0], x[1]], list(x)) for x in dh], reverse=True)
+    sdh = sorted([(arc_scores[x[0]][x[1]], list(x)) for x in dh], reverse=True)
     graph = [[] for _ in range(len(tree_heads))]
     for d, h in enumerate(tree_heads):
         if d:
@@ -59,7 +59,7 @@ def add_secondary_arcs(arc_scores, rel_labels, tree_heads, root_label, parse_pre
     num_root = 0
     for h in range(len(tree_heads)):
         for d in graph[h]:
-            rel = rel_labels[d, h]
+            rel = rel_labels[d][h]
             if h == 0:
                 rel = root_label
                 assert num_root == 0

config.graph.template.jsonnet

@@ -114,8 +114,10 @@ assert pretrained_tokens == null || pretrained_transformer_name == null: "Can't
         use_sem: if in_targets("semrel") then true else false,
         token_indexers: {
             token: if use_transformer then {
-                type: "pretrained_transformer_mismatched",
+                type: "pretrained_transformer_mismatched_fixed",
                 model_name: pretrained_transformer_name,
+                tokenizer_kwargs: if std.startsWith(pretrained_transformer_name, "allegro/herbert")
+                                  then {use_fast: false} else {},
             } else {
                 # SingleIdTokenIndexer, token as single int
                 type: "single_id",